Facial Recognition Technology for Identification
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Facial recognition is a crucial factor of everyday identification processes: human beings recognize and evaluate each other by means of the face. Whenever driving licences, identity and membership cards are checked or wherever access is controlled by security staff, the identity is verified by looking into somebody's face. Thus, unlike other biometric features, e.g. the fingerprint or iris recognition, facial recognition is a transparent procedure well-known to human beings. However, especially in the context of the international fight against terrorism it has become obvious that the traditional way of identifying individuals is insufficient. There are certain limits to the natural recognition process carried out by human beings: The recognition performance is not only impaired by difficulties with the recognition of people from other ethnic origin or deceptions due to a different hair-do or beards, but also by subjective impression based on a person's outward.
The requirement of successful personal identification in access control and in other cases leads to using the results of biometrics. Biometrics Face recognition is a passive, non-invasive method for verifying the identity of a person, Offers the benefits of its unique facial technology in the form of customized overall solutions for the areas of access control, border control, ID-Management, search for criminals and video surveillance
Face recognition has come to be an active research area with numerous applications in recent years. In this thesis, a variety of approaches for face recognition are reviewed first. These approaches are classified according to basic tasks i-e Face Detect, face Normalization, and Face recognition. Then, an implementation of the face recognition method, the Eigenface recognition approach is presented in detail as well as other face recognitions methods i-e Local Feature Analysis, Neural Networks and Automatic face processing are discussed in general.
Ever since the birth of first mankind, human beings have continually been seeking for personal possessions. From the very basics of food and clothes, to cars, houses, and the more recent substantial property of data and information, it is becoming increasingly important that such valuable assets be sheltered by means of security control.
Throughout history, the types of technologies used on the access control systems are countless. From the traditional systems such as security guards checking personal ID's to the very fundamentals of keypads and locks and password or entry code, the focus now has moved to the more advance technologies, particularly in today's multifaceted society. Organisations are continuously seeking for a more secure, suitable and economical way of property protection.
The problem associated with traditional mechanisms is that the possessions could be lost, stolen, forgotten, or misplaced. Furthermore, once in control of the identifying possession, any other "unauthorised" person could abuse the privileges of the authorised user. Therefore there is a need of another approach to properly differentiate the correct (right) person from an impostor by positive identification of the person seeking access. Biometrics is one rising development in the field of access control system that provides true identification. Although the word ``biometrics'' sound very new and high tech, it is in fact the oldest form of identification known to man. Since the dawn of man, a persons face and voice was used to identify him/her. Before the digital age, a hand written signature was the only method used by a person to assert a unique form of identification that was difficult to copy. Popular biometric systems in use today include fingerprint recognition, iris recognition, voice recognition, and facial recognition systems. These systems are in practice in different organizations like banks, airports, social services offices, blood banks and other highly sensitive organizations. Biometric system offers the most accurate authentication solution and convenience. Biometrics systems can be integrated into any application that requires security, access control, and identification or verification of people. With biometric security, we can dispense with the key, the password, the PIN code; the access-enabler is human beings - not something he/she know, or something in his/her possession.
This part of the dissertation provides the general overview of biometrics. Definitions such as 'Automatic', 'Physiological' and 'Behavioural' characteristics are also discussed as well as different types of biometric systems i.e. one-to-one and one-to-many. General Biometrics Base systems model, how it works and Multimodal Biometrics systems are also discussed in detail.
In the last section of this chapter, a comprehensive overview of the right approach in selection of different technologies for an origination in terms of business objective, user acceptance, FFR, FAR, organisational environments, cost and a comparison of all biometrics are also presented.
Different types of biometric technologies are described in this chapter i.e. finger prints, iris and retina, voice, biometric signature and how these technologies work and the main features of these technologies with the help of diagrams.
This chapter is one of the most important chapters which explain the general back ground of face recognition technology and how face recognition works. It gives a brief discussion of how verification and identification is achieved with the help of face recognition system.
Actual techniques involved during face verification and identification i.e. faces detection, face normalisation and face recognition are also discussed in detail. Steps involved during the face detection i.e. coarse detection phase and refined search phase are discussed as well as how Normalisation is achieved through different steps i.e. lighting normalisation, scaling normalisation, rotation normalisation and background subtraction.
Face recognition and methods of face recognition i.e. Eigenfaces, feature analysis, neural network and automatic face processing are discussed in this presentation.
In this chapter of my dissertation, a proposed model of face recognition system for attendance of university students is discussed. The specification of the system is also compiled after the extensive study of face recognition products of different Vendors.
This final chapter of my dissertation contains the conclusion, future work and issues involved with face recognition system.
A review of the biometrics technology
Biometrics: An overview
In today's networked and digital world the role of system security has a vital importance. In originations a large number of professional people are involved in one form of electronic transaction or another. Securing a company's digital assets and identities is a necessity for financial success. Ignoring IT security increases the risk of losses for any company moving through this electronic world.
Logging on to a system by entering user ID and password is very simple but its simplicity makes serious security problems. There are, however, people who use 'easy guess' passwords or leave written passwords near to their computer. In this situation there is no way to confirm that the person is logged on the system using his/her ID and password or some one else, nothing can prevent someone else from accessing sensitive material. It's like a passport system that doesn't require a photograph. In addition, time consuming tasks behind the management of user ID and passwords divert already insufficient resources from other important responsibilities.
Establishing an accurate identity is the main focus of the information systems security in recent years and great efforts are made in this field. Two types of identification systems are in use now today.
- In one type identification system flawed identity checking results in unnecessary duplication, fraud and client disruption, resulting costs and risks.
- While in other type of identification system an accurate identification procedure and effectiveness may be undermined by unpopularity resulting falsification and evasion.
Three conventional forms of identification are in use.
- Origination ID or smart cards.
- The use of passwords or Personal Identification Number's, mother name, place of birth, home address etc.
- The third form of identification is to identify something unique about a person, such as fingerprints, voice recognition, hand geometry, face structure, iris and retina. This third form of identification is known as 'Biometrics'.
Biometrics is a branch of science in which we study, what makes us biologically unique. It is also referred to the science and application of statistical analysis of biological characteristics (Physiological/ Behavioural). In security terms, Biometrics refers to technologies that analyse human characteristics for security purposes. Therefore Biometrics technologies are concerned with the physical parts of the human or personal trait of human being.
There are different definitions of security base biometrics that have been circulating for a numbers of years.
According to Ashbourn, an expert in Biometrics, "Biometrics is a measurable physiological and / or behavioural trait that can be captured and subsequently compared with another instance at the time of verification"). 
The Biometrics Consortium states "Biometrics is automated methods of recognizing a person based on a physiological or behavioural characteristic". 
The international Biometrics Group defines biometrics as "the automated use of physiological or behavioural characteristics to determine or verify identity" 
- Physiological characteristics are fingerprint, Hand geometry, iris pattern ,retinal, ear shape and facial scans etc
- Behavioural characteristics are voice pattern, key strokes, signature etc.
As mentioned, biometric technologies are anxious with the physical parts of the human or personal mannerism of human beings. The word "automatics" basically means that biometrics technology must recognise to identify /verify human characteristics rapidly and automatically, in real time.
Unique physiological characteristics or behavioural mannerisms are examined in biometrics verification for an individual's identity. Physiological characteristics are essentially unchangeable such as hand geometry, iris pattern , palm prints, face structure and vane shape etc .while behavioural characteristic such as one's signature, voice or keystroke dynamics are changeable and these behavioural characteristics can change over time. They are both controllable and less controllable actions.
The initial sample of the biometrics template, which is stored in the data base during the Enrolment, must be updated each time it is used. Although behaviour characteristics based biometrics is less costly and less intimidating to users, physiological characteristics have a tendency to offer greater accuracy and security. In any case, both techniques grant an extensively higher level of identification and verification as compare to smart cards or passwords technologies.
A password or personal identification number (PIN) is not unique for an individual ,it can be stolen ,forgotten or lost, while a biometric characteristic is unique to each individual; it can be used to prevent fraud or theft. It cannot be lost, stolen or forgotten.
There already many places such as research laboratories, defence (military) installations, VIP offices, day care centres and cash points where access is guarded by biometrics base authentication system.
The following biometric identifiers currently obtainable or under development are fingerprints, body aroma, ear shape, face recognition, keystroke dynamics, palm print, retinal scan, iris pattern, signature, DNA, "vein check" and voice pattern.
A biometric based system is a system that in some way uses physical characteristics or personal traits of a human being. These systems are not only, mainly used for security, but also use for encryption.
The processes of translating a message (plaintext), with the help of software, into a programmed message/encoded text (Cipher text), called Encryption. This is usually accomplished using a secret key and a cryptographic code. 
Type of Biometrics-based Systems
There are two types of Biometrics-based systems.
One-to-one systems (Verification system)
One-to-many systems (Identification System)
One-to-one system (verification)
This type of biometric system works on the base of one to one matching and authentication principles where the system asks and attempts to answer the question "Am I who I claim to be?" At first a biometric sample of a person is provided to the system and then the system matches this sample to the previously stored template during the enrolment mode for that person. The system then decides whether that is the person who claims the identity. After a successful matching of the fresh sample with the stored template, the system authenticates the person. These types of systems are also referred to as verification systems. The verification system is a fast response system because it minimises the use of resources and time by providing biometrics sample/ information to the system which specifies the stored template in the data base for that person. 
One-to-many system (identification)
This type of biometrics system works on the base of one to many recognition principles. The system attempts to answer the question," Who am I?" The basic purpose of this system to identify a person's identity by performing matches against all biometrics templates stored in a data base or a data library. A person does not claim his/her identity to the system; instead the person just gives the system some biometric data. The system then performs to match this data to all templates previously stored in the database and decides whether a match can be made. It is not necessary that the system responds with the person's name, it could be the person's ID or other unique identity. These types of systems are referred to as identification systems . Identification systems have a slow response as compared to verification systems. This is because they require much more powerful resources due to the fact that more comparisons are required by identification systems.
The biometrics identification system also prevents a person from registering twice on the system and ensures that a person is not already present in a data base. This type of system can be used in a large scale public benefits organisation, such as being used at banks where a person would try opening a second account on another name. This system can also be used with immigration where a person could try to enter the country on false documents.
General Biometrics Base Authentication System Model
A general biometrics base authentication system model consists of three major components, hardware, software and interface. Hardware is used to capture the biometrics information and software is used to maintain and manage it while an interface with application system that will use the result to confirm an individual's identity. The system operates in two different modes:
- Enrolment mode
- Authentication mode
In this mode a user's biometrics data is provided to a system, which stores this user's biometric sample in a database or data library as a template. Hardware such as a biometrics readers/ scanners, cameras are used to capture biometrics sample. This stored template is then labelled with a user identity e.g. name, identification number etc.
The way biometrics operate
Some biometric base authentication systems may need a number of biometrics samples in order to build a profile of the biometric characteristics. These exclusive characteristics are then extracted and changed in to mathematical code by the system. Which is then stored in to the biometric system as a biometric template for the person who enrolled? The template is store in the memory storage of the system, or in computer database, smart card or barcode. A threshold is set in to the biometrics base authentication system according to the level of security , (a high threshold is set for high level of security)
To secure the template to the person, a trigger or other mean of securing such as personal identification number, or a smart card that store the template which read by a card reader during the authentication mode, are use in biometrics. In some biometrics system when ever a person interacts with the system a new biometrics sample is provide to the system which is compared to the template. If this new sample and stored template is match (the score of new match if exceed from the set threshold then access is granted to that person).
As both physical and behavioural characteristics are inconsistent with time, this change may be due to the age of the person, general health condition, working and environmental conditions and time pressures etc. the biometric base authentication system must allow for these delicate changes, in this case before a match is recorded a threshold *1 is set. This can take the form of an accuracy score *2. The comparison between the template and new sample must exceed this set threshold. If it not exceeds the system will not record the match and will not identify the person.
This use of a threshold gives biometric technologies a significant advantage over passwords, PIN's and ID badges. The use of a threshold affords a tremendous degree of flexibility and if the comparison between the new biometric sample and the template exceeds the stated threshold, identity will be confirmed.
- Threshold:-a predefine number, often controlled by system administer, which establish the degree of correlation necessary for a comparison to be deemed a match.
- Score: - A number indicating the degree of similarity or correlation of a biometrics match
Capture, extraction, comparison and match/non match are the four stages use by all biometric authentication systems.
- Capture - A physical or behavioural sample is captured by the system during enrolment.
- Extraction - unique data is extracted from the sample and a template is created.
- Comparison - the template is then compared with a new sample.
Multimodal Biometric System
In some environments a signal biometrics identifier base system such as finger scan, face scan or iris scan etc often not able to meet the desired performance requirement of the organization. Different biometrics base identification system such as face recognition, finger print verification and vice verification, is integrated and worked as a single biometrics base identification system. Multimodal biometrics base identification system is use to over come the limitation of the single identifier biometrics base identification system.
Initial experimental results reveal that the identity established by such an integrated system is more reliable than the identity established by a signal biometrics identifier base system. 
Selecting the Right Approach
In Different Environment Different biometrics base authentication systems are used. To choose the right approach to biometrics authentication it is necessary to understand the requirement of the organisation, the application of the biometrics system, and characteristics of the biometrics devices itself.
Following factors are also important to choose a biometrics base authentication system, which most devices can't store raw fingerprints and that fingerprints can't be reconstructed based on the data stored within these systems. Intrusiveness is another factor affecting user acceptance of some devices, particularly iris and retinal scanning systems. 
Business objective of the organisation
The most important aspect to consider when selecting a biometrics base authentication system is the organisation business' objectives. The choice biometrics system must meet or exceed organisational business objectives as well as sustain organisation in the coming years. Business objective is the bottom line where organisation starts and end.
Some biometrics, such as fingerprints, may be apparent as an assault of personal privacy. The system must not associate with other govt agencies biometrics (finger print) recognition system that most devices can't store raw fingerprints and that fingerprints can't be reconstructed based on the data stored within these systems. General intrusiveness can be another factor affecting user acceptance of some devices, particularly iris and retinal scanning systems. Following are the errors of biometrics base authentication system.
False acceptance rate (FAR)
False acceptance rate (FAR) is a system error. It is the rate at which an interloper can be recognized as a valid user. In one -to-one match during user verification, false acceptance is based on fake attempts, not on the total number of attempts by valid users.
If FAR is 1%, it means one out of 100 users trying to break into the system will be successful . FARs become more critical when you attempt to identify users based on biometrics, instead of simply trying to verify a person with a one-to-one or one-to-few operation
False reject rate (FRR)
False reject rate (FRR) is another type of error of biometrics system. It is the rate at which a valid user is rejected from the system. Consider a finger print recognition system; unfortunately, the conditions under which the original sample was collected can never be exactly duplicated when the user submits subsequence biometrics information to be compared. False reject rate may occur due to following variations.
- Rotation and Translation because of different positioning of the finger on the finger print device.
- Downward pressure on the surface of the input device which changes the scale of input device.
- Non-permanent or semi-permanent distortions like skin disease, scars, sweat, etc
To over come FRR it is essential that all biometrics base authentication systems have a threshold value in order to allow for minor differences.
With out threshold value FRR occurs and valid users will be probably rejected by system. If the threshold value is too high FAR occur . It is there for necessary to find a proper threshold value.
As stated it is important to consider the organisational environment when selecting biometrics base authentication system. Users with wet, dirty or dry hand have experienced problems with finger and palm recognition system. People using gloves generally can't use these systems. Face recognition system can't be easily be used in medical environments where hood and masks are used by users.
The direct cost of the system (hardware and software) is the initial considerations. Due to the improvement of features and functionality the over all cost of biometrics system reduces. It not only reduces fraud and eliminating problems associated with stolen or forgotten passwords but also reduces the help desk role.
The subject of this chapter is biometrics, which is defined as "...a method of verifying an individual's identity based on measurement of the individual's physical feature(s) or repeatable action(s) where those features and/or actions are both unique to that individual and measurable".
A biometrics system which consists of enrolment mode and authentication mode, unique physiological characteristics or behavioural mannerisms are examined in biometrics verification for an individual's identity. All biometric systems essentially operate in a similar way in a four-stage process that is automated and computerized which are Capture, Extraction, Comparison and Match/non-match.
Biometrics system one-to-one is based on one to one matching and authentication principles and is mainly used for verification purposes, while biometrics system one to many works on the principles of one-to-many recognition and is used for identification.
Multimodal biometrics base identification system is used to over come the limitation of the signal identifier biometrics base identification system in which different biometrics base identification system such as face recognition, finger print verification and vice verification, is integrated and worked as a single biometrics base identification system.
Methodologies of Biometrics Authentication
As stated, different biometric systems are use in different organisations according to their requirements. The most common biometrics system in use today includes fingerprint recognition, iris recognition, and voice recognition and face recognition systems. There are also other biometric systems available like retina recognition, vein pattern recognition, signature and DNA matching systems. These systems are not as widely used yet for various reasons.
These biometrics systems can be integrated into any application that requires security, access control and identification or verification of people. With biometric security we can dispense with the key, the password and the PIN code; the access-enabler is a person, not something person know or something in his /her possession. Biometrics systems secured resources are based on who a person is. Biometrics systems also minimise the risk that is associated with less advanced technologies while at the same time offering a higher level of security and convenience.
Fingerprint Recognition System
Fingerprints are one of the human physiological characteristics that do not change throughout someone's life. Even identical twins have different fingerprint patterns. The chance of identical twins to have the same fingerprint is less than one in a billion. Fingerprint recognition is generally considered the most practical system for its reliability, non-intrusive interfaces, and cost-effectiveness. In recent years, fingerprints have rallied significant support as the biometric technology that will probably be most widely used in the future. In addition to general security and access control applications, fingerprint verifiers are installed at different organisations such as, defence/military organisations health care, banking and finance, application services providers, immigration, law enforcement etc.
The fingerprint's strength is its acceptance, convenience and reliability. It takes little time and effort for somebody using a fingerprint identification device to have his or her fingerprint scanned. Studies have also found that using fingerprints as an identification source is the least intrusive of all biometric techniques. 
Verification of fingerprints is also fast and reliable. Users experience fewer errors in matching when they use fingerprints versus many other biometric methods. In addition, a fingerprint identification device can require very little space on a desktop or in a machine. Several companies have produced capture units smaller than a deck of cards.
One of the biggest fears of fingerprint technology is the theft of fingerprints. Skeptics point out that latent or residual prints left on the glass of a fingerprint scanner may be copied. However, a good fingerprint identification device only detects live fingers and will not acknowledge fingerprint copies.
Main Feature of Finger print verification system
- Analysis of minutia points i.e. finger image ridge (verification) endings, bifurcations or branches made by ridges.
- One of the most commercially successful biometric technologies.
- Important for applications where it is necessary to verify the identity of those who gain access.
How fingerprint recognition system works
In biometrics systems fingerprint recognition system is the fastest verification /identification (One-to-One / One-to-Many) system as shown in figure 3, 4, 5. Like other biometrics recognition systems it performs fingerprint recognition with the help of specialised hardware. This specialised hardware is supported by the conventional computer hardware and special software. All biometrics systems operate in two modes, enrolment mode and authentication mode (as discussed in the previous chapter). A sample of the fingerprint of a live person is provided to the system which is then converted into mathematical code (Template) and stored for the enrolee into the database.
In the first step of the authentication process, a fingerprint impression is provided to the system. The system takes a digital image (input image figure 3:1:1 below) using different techniques including scanner, optical, and ultrasound or semiconductor chip technologies. The digital image of the fingerprint includes several unique features in terms of ridge bifurcations and ridge endings, collectively referred to as minutiae. 
In the next step the system uses an automatic feature extraction algorithm to locate these features in the fingerprint image, as shown in Figure 3:1:2.
Each of these features is commonly represented by its location (x, y, and z) and the ridge direction at that location; however the feature extraction stage may miss some minutiae and may generate spurious minutiae due to sensor noise and other variability in the imaging process. The elasticity of the human skin also affects the feature extraction process. 
In the final stage, a final decision of match and non match is made on the bases of similarity between the two sets of features after compensating for the rotation, conversion and dimension. This similarity is often expressed as a score. A decision threshold is first selected. If the score is below the threshold, the fingerprints are determined not to match; if the score is above the threshold, a correct match is declared an authentication is granted to the person.
Iris and Retina Recognition System
Biometrics which analyse the intricate and unique characteristics of the eye can be divided into two different fields, Iris and Retina. Iris and retinal scans both deal with the human eye. They are done in an extremely different way as compared to other biometrics technology.
Iris Recogniton System
Iris recognition biometrics base authentication systems have unique characteristics and features of the human iris used to verify the identity of an individual. The iris is the area of the eye where the pigmented or colour circle, usually brown or blue, rings the dark pupil of the eye. It consists of over 400 unique distinguishing characteristics that can be quantified and used for an individual identity. However, only about 260 of those characteristics are captured in a "live" iris identification process . Iris' are composed before birth and, except in the event of an injury to the eyeball, remain unchanged throughout an individual's lifetime. Eyeglasses and contact lenses present no problems to the quality of the image and the iris recognition /scan systems test for a live eye by checking for the normal continuous fluctuation in the pupil size. As Iris patterns are extremely complex and unique they carry an astonishing amount of information. The fact that an individual's right and left eye are different and that patterns are easy to capture, it establishes iris recognition /scan technology as one of the biometrics that is very resistant to false matching and fraud.
The false acceptance rate for iris recognition systems is 1 in 1.2 million, statistically better than the average fingerprint recognition system. The real benefit is in the false-rejection rate, a measure of authenticated users who are rejected. Fingerprint scanners have a 2 percent false-rejection rate whereas iris scanning systems boast rates at a 0% level .
How Iris recognition systems work
Like other biometrics base authentication systems it consists of two modes, enrolment and authentication mode. In the iris recognition/scan process a photograph of the eye is taken with the help of a specialised camera, typically very close to the subject (eye), no more than three feet. This specialised camera uses an infra-red imager to illuminate the eye and capture a very high-resolution photograph. This process takes only one to two seconds and provides the details of the iris that are mapped, recorded and stored for future matching/verification.
Two types of methods are used in iris recognition process.
- Active iris recognition process
- Passive iris recognition process
In Active iris identification method the distance between the user and the camera must be between six and fourteen inches. It also requires the user to move back and forth so that the camera can adjust and focus in on the user's iris. The passive system differs as such that it allows the user to be anywhere from one to three feet away from the integrated series of cameras that locate and focus in on the iris.
In the identification process a wide-angle camera calculates the position of the eye. When a person stands in front of the iris identification system, between one and three feet away, a second camera zooms in on the eye and takes a black and white image. Once the iris is in focus, it overlays a circular grid on the image of the iris and identifies the light and dark areas, like an "eye print". The captured image or "eye print" is checked against a previously stored reference template in the database.
The inner edge of the iris is located by an iris recognition /scan algorithm which maps the iris' dissimilar patterns and characteristics.
An algorithm is a series of commands that tell a biometric system how to interpret a specific problem. Algorithms have a number of steps and are used by the biometric system to verify if a biometric sample and record is a match.
Retina Recogniton System
The retina is the layer of blood vessels at the back of the eye.
Like iris recognition biometrics base authentication systems, Retina recognition /scanning are also an extremely accurate biometric system. The patterns of blood vessels at the back of the human eye are unique, and it remains the same throughout ones life.
How Retina recognition system works
Retina scans are performed by directing a low-intensity infra-red light to capture the unique retina characteristics. In order for retina scan devices to read through the pupil, users must situate their eyes within three inches of the capture device and hold still while the reader finds the blood vessel patterns. They must also focus their eyes on a single point of light to obtain a successful reading.
An area known as the face, situated at the centre of the retina, is scanned and the unique pattern of the blood vessels is captured. Retina biometrics is considered to be the best biometrics performers. However , despite its accuracy, this technique is often thought to be inconvenient and intrusive, hence, it is difficult to gain general acceptance by the end user. The retinal scanner requires an individual to stand still while it is reading the retinal information. Eye and retinal scanners are ineffectual with the blind and those who have cataracts. 
Main feature of Iris and Retina Recognition system
- Analysis of the iris, which is the colure ring of tissue that surrounds the pupil of the eye.
- This is a highly mature technology with a proven track record number of application areas.
- The retina is the layer of blood vessels situated at the back of the eye. The scanning technique to capture data from the retina is often thought to be the most inconvenient for end-users.
- An end user must focus on a green dot, and when this has been performed, the system uses a harmless beam of light to capture the unique retina characteristics.
Voice Recognition system
Speech is the primary and most natural form of communication among humans. Because of this and the fact that speech is a primary form of personal recognition, people commonly have no problem accepting it as a biometric. Voice recognition based personal identification is one of the oldest and better-accepted biometric technologies. Voice recognition technology is mainly use for verification .it is easy to use, less expensive and non-threaten to the user. Voice recognition is the only technology that offers remote personal identification with existing resources. However, if a personal recognition system developer requires "identification mode" operations, then other recognition technologies such finger print, iris, retina etc, will probably need to be considered. 
Advantages of using voice as a biometric includes:
- Simple to use,
- feels natural to the user,
- provides eyes and hands-free operation,
- can easily be implemented to support remote recognition
- Implementation is usually inexpensive (often requiring software only).
Typical problems with voice recognition system include:
- different type of environments (e.g. lab environment for enrolment, office environment for recognition )
- channel mismatch (e.g. different microphones for enrolment and verification)
- background noise
As human voice is not rich in discriminative features such as fingerprints and iris patterns, there for voice recognition is a poor candidate for "identification mode" operation when there is a large database of enrolees. 
Permanence of the voice recognition system can affect by change in voice due to aging and disease (e.g. stress, colds, and allergies). A system that updates the user's speaker model with each successful identification/verification might compensate for the changes.
How Voice Recognition System works
Like other biometrics systems, voice recognition systems also operate in two modes enrolment and authentication during the enrolment some sounds, words or phrases spoken by humans into a microphone are converted into electrical signals (from analog signal in to digital signals) by voice recognition system with the help of A/D converter  and then these signals are transformed into coding patterns (template) to which meaning has been assigned and store in to database which is referred to each time that person attempts to access secure data.
When a user attempts to gain access to the secure data need to speak a phrase, the words are extracted and compared to previously stored voice models (template) and all other voice prints stored in the database. Each speech sound in the user's phrase is queried in an anti-speaker database. Since some characteristics of a person's voice are the same as another's, the system authenticates the user by comparing the user's common features with those in the anti-speaker database and eliminating those common elements from the sample to be authenticated. When all features matching others are removed, the system is left with only the unique features of the user's voice. These unique features, compared with the enrolled phrase, are the characteristics which determine successful authentication.
Main Features of Voice Recognition System
- Analysis of the unique characteristics of voice as a merger of physical and behavioural characteristics (physical dimensions of the voice box and the sounds adopted as speech is learned).
- Very little hardware is required (a microphone on a standard PC with software to analyze the unique characteristics).
- Ideally suited to telephone-based applications.
Signature verification is the process used to recognize an individual's hand-written signature. Biometric signature is a term used to refer to a signature that has been recorded/captured using a selection of input devices such as scanners, personal digital assistants, computer displays or other contact etc. This method allows real handwritten signatures to be incorporated into e-documents during electronic transactions. Not every technology captures signature information the same way. Signature capture is becoming fairly accepted as a replacement for pen and paper signing in bank card, PC and delivery-service applications. Biometric Signatures identification is also known as Dynamic Signature Verification (DSV) . This verification analyzes the way a user signs his/ her name. As signatures is used a means of transaction-related identity verification, among the peoples, most would see nothing unusual in extending this to cover biometrics. Signature verification devices are sensibly accurate in operation and clearly lend themselves to applications where a signature is an accepted identifier.
In biometrics signature verification, important features of the finished signature such as speed, velocity, and pressure are use during the signature verification by verification devices. Verification devices use wired pens, pressure-sensitive tablets, or a combination of both. Devices using wired pens are less expensive and take up less room Currently, tablet-based systems that operate using off the shelf digitizers cost as little as £70-£100. Over 100 patents have been issued regarding signature verification to companies such as IBM, National Computer Register, and VISA. . although it's less expensive but are potentially less durable. So far, the financial community has been slow to accept automated signature verification methods for credit cards and check applications because signatures are still too easily forged. This keeps signature verification from being incorporated into high-level security applications. The major advantage of the Biometric signatures is if any process that requires a signature is a prime contender for signature identification. Individuals are less likely to object to their signature being confirmed as compared to other possible biometric technologies. Biometrics signature technology is also represents an ideal, bridge between the long-recognized convention of signing a document and the need for electronic documents to be uniquely recognized by individuals. This application provides individuals with security and control on documents originated, transacted and stored in the digital domain.
How Biometric Signatures works
In Biometrics or Dynamic Signature identification systems the primary components of signature verification are specific feature of the process of the signature (behavioural component) such as stroke order, speed and pressure, and the difference of specific feature of the signature ( visual Images). There is an important distinction between simple signature comparisons and dynamic signature verification. Both can be computerized, but a simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made .
Like other biometrics identification systems, in this system the devices convert the features of a signature in to a template and store it for future comparisons in their database. Signature identification devices can also analyze the static image of one's signature, which captures the complete image of one's signature and stores it for comparison. These devices account for changes in one's signature over time by recording the time, history of pressure, velocity, location, and acceleration of a pen each time a person uses the system. With biometric signatures, the authentication can be done in real-time or after the fact. In the event that a biometric signature is contested, the signature data can be extracted from the document and submitted to the verification devices for investigation and analysis to verify the authenticity of the signature. 
The major problem faced with this technology is differentiating between the consistent parts of the signature and the behavioural parts of the signature that vary with each signing. An individual's signature is never entirely the same every time it is signed and can vary substantially over an individual's lifetime. Allowing for these variations in the system while providing the best protection against possible forgers is an apparent hurdle faced by this technology.
Main Features of Biometrics Signature
- The movement of the pen during the signing process rather than the static image of the signature.
- Many aspects of the signature in motion can be studied, such as pen pressure, the sound the pen makes against paper, or the angle of the pen that makes this a behavioural biometric.
- We learn to sign our name, and our signature is unique because of this learning process. The speed, velocity, and pressure of the signature remain relatively consistent.
Each biometric product either under development or commercially available in the market can be categorized as falling into one of the biometric technology areas.
The main biometric technologies are fingerprints, Iris, Retina, voice and digital signature. In finger print technology finger print is used as a biometrics sample while Iris and Retina are exam in Iris and Retina technology .In voice biometrics technology a pattern of voice is matched with previously store voice template in the database. The stroke order, speed, pressure and the difference of specific feature of the signature is verified in Biometrics signature system or Dynamic Signature identification systems.
Face Recognition system
General Background of Face Recognition system
Face recognition systems identifies an individual by analyzing the unique shape, pattern and positioning of facial features. In other biometrics technologies, Face recognition technology is fairly young .it has been object of much interest in last few years. Facial recognition is also the most common method of human identification. It is non-invasive, usually passive, and fairly inexpensive, people generally do not have a problem accepting it as a biometric. It is probably for these reasons that face recognition has been one of the most active areas of biometric research. To improve and rebuts this technology, many companies pushing for the development of this biometric technology. Face recognition technology involves analysing certain facial characteristics, storing them in a database and using them to identify users accessing systems. There are various recognition methods that emphasize identification based on the areas of the face that don't change, including: upper sections of eye sockets, area surrounding cheek bones and sides of mouth. Making use of unique features or characteristics of the human face, often irrespective of facial hair or glasses, facial scan is deployed in fields as varied as physical access, surveillance, PC access, and cash point access. This system can also recognize faces within a crowd in the attempt to match them to stored images of known criminals. With the recent terrorist attacks there has been a tremendous push to implement this technology in a variety of public places such as airports, government buildings, border crossings, banks, public transport stations and other vulnerable areas. This technology is also use by law enforcement agencies in high crime areas like the streets of Tampa, London borough of Newham as well as other different cities. The use of this technology is also including identification systems for such things as security sensitive areas such as Nuclear power plants, cash points etc. This system is also use in many casinos to scan for cheaters and dishonest money counters. Face Recognition also provides the ability to reduce fraud and crime by identifying duplicate images in large databases, such as licensed drivers, benefit recipients, missing children and immigration. If the system is fully deployed it will contain up to 20 million images with the ability to retrieve images within seconds. 
This biometrics base, Face recognition system can easily integrated with existing CCTV cameras with the help of software which can be used to monitor the criminals in area.
The impatience to implement face recognition can be somewhat explained by its relatively cheap price. Recognition software can run as cheap as a few thousand pounds, and with the ability to utilize PC cameras the cost is significantly lower than other biometrics hardware.
Main Features of Face Recognition System
- Analysis of the unique shape, pattern, and positioning of facial features.
- Highly complex technology and largely software based.
- There are essentially two methods of capture: using video or thermal imaging. The latter is more expensive because of the high cost of infrared cameras.
- Primary advantage is that the biometric system is able to operate "hands-free," and a user's identity is confirmed by simply staring at the screen.
- Continuous monitoring of the user.
- Access to sensitive information can be disabled when the user moves out of the camera's field of vision.
- Verification is then performed when the user returns to work at the desktop.
How Face Recognition Systems works
Face recognition technology involves analyzing certain facial characteristics, storing them in a database and using them to identify users accessing systems. There are various recognition methods that emphasise identification based on the areas of the face that don't change, including: upper sections of eye sockets, area surrounding cheek bones and sides of mouth. Like others Biometrics Technologies Face Recognition Technologies also operate in Enrolment and Authentication modes. During the enrolment mode several
pictures are taken of one's face as a biometrics sample. This process will generally consist of a 20 -30 second . Ideally, the series of pictures will incorporate slightly different angles and facial expressions, to allow for more accurate searches. After enrolment, distinctive features are extracted, and converted in to mathematical codes called Template. This Template is then store to the data base. The template is much smaller than the image form which it is drawn.
In verification mode(one to one operation), an individual approaches the checkpoint and presents an identity using a smart card, proximity card, PIN or other identity card have person image. The face recognition system take images, this captured images of the individual's face is converted in to a template (the mathematical process used by the computer to perform the comparison), after the template is generated, it is match with the store template for that person, if the score exceed from the set threshold then access granted. Threshold can be adjusted for different personnel, PC's, time of day, and other factors.
Duane M. Blackburn1 August 10, 2001
In identification mode (one-to-many operation) usually operate by comparing new unknown images with a set of known images, often referred to as the gallery or imge library. The unknown, or probe images, are usually obtained some time after those in the enrolled gallery. In identification setting the new image may belong to a person who is, or is not, already present in the gallery. Following three modules are the basic component of the face recognition system
- Face Detect
- Face recognition
In order to make a perfect database for the recognition modules it is necessary to locate the exact position of the face in the image. Before recognition stage the face must be detected, extracted and normalized whether the input image is to be tested for identity or added into the database.
The goal of face detection is to determine whether or not there are any faces in the image and, if present, return the image location and extent of each face . Face detection and normalization depends on each other. As each module requires the intermediate output of another module to continue with any further processing that's why they can not work individually. Two phases have been designed in face detection process i.e.
- Coarse detection phase
- Refined search phase
Course detection phase.
In coarse detection phase a quick scan over the complete image is performed in order to analyse the colour content of the input. To locate the exact position of the face by applying refined search techniques in the second phase of face detection, which is performed after normalization of the luminance, the search space is reduced in this phase by identifying the skin regions in the image.
Some basic steps are also involved in the face detection processes which are skin detection, Colour segmentation and ultimate erosion
Human skin colour has been used and proven to be an effective feature in many applications from face detection to hand tracking, although different people have different skin colour. Before entering into the face recognition system all the images or scenes from a video must pass through the first stage of the face detection module i.e. skin detection. In this stage a fast coarse search of the scene is performed in order to locate skinned regions in the image, so that the non-skinned regions can be removed. The purpose of this first stage is to perform a fast coarse search of the scene in order to locate probable skinned regions in the image, so that non-skinned backgrounds can be removed with the knowledge that the face will not be located in those regions of the image. A smaller image can then be extracted from the scene, such that successive searches for the precise location of the face can be performed on a reduced search space rather then the entire image. In this way the speed of processing and the accuracy of the location of the face are increased by removing the probability of the error and reducing possibility of FRR and FAR. Although skin colours vary between different people and different races but it has been verified that human skin tones form a special group of colours, unique from the colours of most other natural objects.
In Colour segmentation, first each pixel is classified as either skin or non-skin to perform skin region analysis. A down sampled version of the image is used in order to increase the speed of this module. For an input image of size 80 by 240, a down sampled rate of four is sufficient, such that the skin detection module only needs to operate on a 20 by 60 image . It has been observed that the accuracy of the search dramatically degrade if down sampling by a factor higher then four is applied . To classify the skin ness of each pixel a reference table is used, where each intensity is checked to see if it falls in the range of skin colour and a give it a binary value of one if it is and zero if not.
In this operation(colour segmentation a bounded box is needed to determine the range and location of the values of ones, after the colour image has been mapped into a binary image of ones and zeros representing skin and non-skin regions.
As the basic purpose of the colour segmentation is to reduce the search space of the following modules, there for it is essential to determine as tight a box as possible without cutting off the face. During the colour segmentation values that are nearly skin but non-skin or skin-like coloured regions that are not part of the face or the body are usually returned. These unwanted values which would be represented by a big connected region in the binary image are generally isolated pixels or group of pixels that are considerably smaller then the total face regions. The addition of these unwanted pixels would result in a box that is much larger than planned and overcome the reason of the segmentation.
To reduce the effects of these unwanted pixels morphological refinements are applied to the binary output . Morphological techniques called erosion and dilations are used to eliminate these unwanted pixels which are generally smaller than the face region itself. Erosion followed by dilation is used in the system after the colour segmentation to clean up the binary mapping before extracting the skinned region.
Refined Face Search
Luminance normalised skinned region of the image is the input of the second refined face detection stage. This stage is the final stage of the face detection module which involves a refined search for the location of the face. In refined search phase, depending on the status of the database two different algorithms are used. Normalised cross correlation is used to find out the centre location of the face when the database is empty and no faces have been processed by eigenfaces decomposition. In the training stage of the recognition module when a set of eigenfaces has been determined then the refined face searches can be applied by using a face projection search.
The accuracy of the output from the refined search modules will decide how absolutely aligned the images are. Consequently, as the face recognition module requires perfect inputs that fulfil some restrictive criteria, the accuracy of these stages will determine how successful the recognition and the overall system will be. It is therefore very important and a high accuracy rate is of supreme concern.
Two methods namely Normalised Cross Correlation (NCC) and Face Space Projection have been designed for this very important stage of the detection module depending on the status of the face database. Face space search involves projecting the sequence of windows into face space and measuring how "face-like" each window is while Normalised Cross Correlation deals with finding the best match between a template and a sequence of windows. Thus, the input data required and the choice of technique used is the major difference between the two searches and is dependent on what information is available. The Normalised Cross Correlation search requires a template; thus, a typical face or average face must be available in order for Normalised Cross Correlation to work. A set of eigen-faces is required by face space projection in order for each window to be projected into face space. The projection technique cannot be used until the set of dominant basis vectors has been calculated and available for use.
Normalised Cross Correlation
Until a set of face images have been processed by the training stage of the recognition module the set of eigen-faces is not available. Template matching with Normalised Cross Correlation is used to perform the refined face search during the initial setup of the face database. Comparing two windows of the same size and determine its relationship or how closely linked each pixel from one window is from its corresponding pixel in another window is the vital theory behind Normalised Cross Correlation. Two windows under examination which has each of their corresponding pixels matching the closest from all other windows under testing are called maximized correlation value.
Since it is the relative differences of intensity values within the picture which is significant during the matching therefore it is important to find a template that reflects the differing intensities of a face accurately. Thus, the best candidate for the template is the average face. It is a standard template representing the most essential features of faces and contains as little influenced additions as can be found of any face image. As it captures the relative differences in intensities between the features of a face it is therefore the most suitable choice for the template. An average face representative of the current ensemble of face images will not be available during the initial detection phase thus until the training stage, neither the eigen-faces nor an average face will be calculated.
Face Space Projection
In face space projection technique each selected window is projected into face space. It is an accurate and alternative method to perform refined face search. A set of eigen-face templates can be determined by the training stage of the recognition module as soon as a database has been initialized by the Normalised Cross Correlation Given these templates, any subsequent image that passes through the second phase of the face detection module can hence utilize eigen-decomposition rather than normalized cross correlation.
Like Normalised Cross Correlation (NCC) method, the input into this refined search stage which is the normalized skinned region of the original image is treated as the test window. During the search, sections of this test window the size of an eigen-face will then be continually extracted for projection into face space. The primary step is the loading of the average face and the eigen-faces that were saved from the training stage of the recognition module.
Notice that the average face used here is the actual average calculated from the set of input faces that were added into the system, unlike the average face used in Normalised Cross Correlation (NCC), which is foreign to the current database. The projection is accomplished by subtracting the selected section of the test window by the average face when the average face and eigen-faces are available. Since all eigen-faces are calculated from the covariance matrix, which originate from zero mean images therefore it is essential pre-processing step to transform the data into a zero mean image In order to produce a set of weights the zero mean test region is then projected into face space by multiplying it with the loaded set of eigen-faces. Using the weights, by comparing the energy of the window to the energy of the transformed window it is likely to determine how "Face-like" the region is. Large weight values will be recorded and the amount of projections onto face space would be maximised if the region was face-like" since every of the basis vectors capture the main variances in a face. Since the eigenvectors do not clearly represent images that do not reflect a face structure a "non-face-like" region will therefore produce a smaller projection values. The maximum value that the weights can have is theoretically the total energy of the original window itself prior to transformation, that is, every bit of energy is conserved and transformed into face space. 
As mathematically energy is the sum of the squares of the intensity values. Theoretically, the difference between the sum of squares of the transformed window i.e. the weights and the norm of the original window should be zero if the region was exactly centred on the face. Every particular region from the test window will therefore have an related distance from face space recorded, such that the least distance out of all the regions tested will symbolize the nearby matched, the most "face-like" area, in the test window. While operating in dynamic mode Face detection is completed in real-time, repeatedly processing images.
Therefore not only the speed but also the accuracy of this refined search module is important. The quicker the face space search can be, the more extraordinary the performance of the system can be contrary to the Normalised Cross Correlation (NCC) module, which is applied at initialization. To attain such effects a multi-resolution search is therefore employed here. The image is first down sampled where a rapid coarse search can be applied prior to inputting the normalized skinned region into the refined face space search. Now to locate the exact centre of the face, neighbouring pixels around the first estimate will be tested and which will improve on the accuracy of the search.
With the amount of speedup limited by the accuracy
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