Biometric Authentication And Robotics Computer Science Essay

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\begin{abstract}\paragraph{}In this survey biometric technology usage will be identified and then discussed as to how it will help in real world protect its information. Background will be given to show how identification and authentication have developed and why another form of authorization needs to come to the forefront. There are reasons for higher security and biometric authentication will be shown to be the solution to answer this call. And compare and contrast currently highly available Biometrics and their pros and cons as well as currently available systems for each Biometric option. Biometric recognition requires the user to be present at the time of authentication, some biometrics may not be quite enough to separately identify two people. So a combination of biometrics recognition can mostly secure way to identify a particular person. In this survey that kind of combined biometrics recognition systems will be discussed. From this survey the main point is been to discuss about why Robotics is becoming an important role in this Biometric Authentication. In the final chapter will be given to explain currently ongoing research projects on Robotics in Biometric Authentication.

\end{abstract}

\tableofcontents

\chapter{Introduction}

\paragraph{} Normal authentication based on username and password. In a computer application or system users are identified by these parameters. When the biometric authentications of human biometrics will is used for authentication. This concept is included in the level of business applications; these applications include workstation and network access, single sign-on, application logon, data protection, remote access to resources, transaction security and Web security.

\paragraph{}The promises of e-commerce and e-government can be achieved by using strong personal authentication procedures. Secure electronic banking, investments and other financial transactions, retail, law enforcement, health and social services are already benefiting from these technologies. Biometric technologies are expected to play an important role in the personal authentication for large-scale enterprise network authentication environments, point of sale and all forms of digital content such as digital rights management and protection applications in health care.

\chapter{Biometric Authentication}

\vspace{-4mm}

\section{Biometric Authentication}

\paragraph{}

An automated biometric system uses biological, physiological or behavioral characteristics to automatically authenticate the identity of an individual based on a previous enrollment event.

If a biological, physiological, or behavioral characteristic has the following properties…

\begin{itemize}

\item Universality (Every person should possess this characteristic)

\item Uniqueness (No two persons possess the same characteristic)

\item Permanence (Does not change in time, i.e., it is time invariant)

\item Collectability (Can be quantitatively measured)

\end{itemize}

Then it can potentially serve as a biometric for a given application.

There are many examples of biometrics being used or whether the federal and state governments, and government projects, local and foreign. One is to use strong authentication for access to computer systems that contain sensitive information that the military services, intelligence and other critical federal security organizations. Physical access control to restricted areas is another important application.

The overall performance of a biometric system is assessed in terms of its accuracy, speed, and storage. And factors like cost and ease of use also affect efficacy.

\newpage{}

\section{Comparison of Currently available Biometric systems:}

\paragraph{}

\begin{itemize}

\item Fingerprint Recognition

\item Iris Verification

\item Face Recognition

\item 3D Hand Geometry

\item Voice Identification

\item Signature Recognition

\end{itemize}

\begin{center}

\begin{tabular}{ | p{3cm} | p{6.5cm} | p{5.5cm} |}

\hline

Authentication type & Current State & Features \\ \hline

\vspace{3.9 mm}

Fingerprint Recognition &

\begin{itemize}

\item Stability and uniqueness: Based on a century of examination, it is estimated that the chance of two people, including twin, having the same print is less than one on a billion.

\item Fingerprint identification is the most widespread application in biometrics. First commercial system was used in 1971.

\end{itemize}

& \vspace{3.9 mm} Endpoint and junction of print ridges, and position, direction and relation between them

\\ \hline

\vspace{3.9 mm}Iris Verification & \begin{itemize} \item Analyze features found in the colored ring of tissue that surrounds the pupil, use a fairly conventional camera element and require no close contact between the user and the reader. \item As a high accuracy biometrics, iris has more details than a fingerprint. Highly detailed and unique texture will remain stable over decades of life.\end{itemize}& \vspace{3.9 mm}Textures with striations, contraction furrows, pits, collagenous fibers, filament, crypts (darkened areas resembling excavations), serpentine vasculature, rings,and freckles \\ \hline

\vspace{3.9 mm} Face Recognition & \begin{itemize}\item Face is the most common biometrics. Using the whole face for automatic identification is a complex task because its appearance is constantly changing.\item One effective approach may employ rule-based logic and a neural network for the image classification process. The first face system is introduced in 1992.\end{itemize} & \vspace{3.9 mm} Geometric feature - shape and characteristics of finger/hand like size of palm, finger length, width, area, thick and their relationship between fingers etc \\ \hline

\end{tabular}

\end{center}

\vspace{-5 mm}

\begin{center}

\begin{tabular}{ | p{3cm} | p{6.5cm} | p{5.5cm} |}

\\ \hline

Authentication type & Current State & Features \\ \hline

\vspace{3.9 mm} Voice Identification & \begin{itemize} \item Utilizes the distinctive aspects of the voice to verify the identity of an individual. The least invasive of the biometric recognition technologies and the most natural to use is speech system. \item Have the most potential for growth, because it requires no new hardware - most PCs already contain a microphone. \item Just say a phrase, about a second long - any language or dialect - chosen by the user. A typical case is AT and T Smart Card. \end{itemize} &\vspace{3.9 mm} Cadence, frequency, pitch and tone of an individual's voice. \\ \hline

\vspace{3.9 mm} Signature Recognition & \begin{itemize} \item Analyzes the way a user signs his/her name to measures the physical activity of signing. \item It should distinguish between person's habitual parts and those that vary with almost every signing.\item Two methods: on-line and off-line, where wired pens and sensitive tables are needed for on-line signature. \end{itemize} & \vspace{3.9 mm} Behavioral components of the signature, such as shape, velocity, stroke order, off-tablet motion, pen pressure and timing information captured during the act of signing, etc. \\ \hline

\end{tabular}

\end{center}

\newpage{}

\section{Fingerprint Authentication}

\textbf{Strengths}

\begin{itemize}

\item Mature and proven core technology, capable of high levels of accuracy

\item Can be deployed in a range of environments

\item Employs ergonomic, easy-to-use devices \end{itemize}

\textbf{Weaknesses}

\begin{itemize}

\item Cuts and bruises on finger; dry or oily finger; Most devices are not able to enroll a small percentage of users (about 5 to10 present)

\item Liveness detection is a great problem, wear and tear of sensor

\item Since touchable, fingerprint impression is often left on the sensor

\end{itemize}

\subsection{Examples of Currently Available systems}

\paragraph{}

Simple tuning of the too-identifiable fingerprint pattern from the many already known fingerprint patterns are not easy to operate because of the high sensitivity to errors in recording fingerprints (eg due to rough fingers, damaged fingerprint data fields or the way a finger is placed in different areas of a fingerprint scanner that can screen in different orientation or deformation of the fingerprint results during the scanning process). A more sophisticated solution to this problem is to characteristics of the so-called minutiae point (point where the small ledges and capillary lines in a fingerprint branches or ends) an extract from the fingerprint image, and check the alignment between these sets of very specific fingerprint characteristics.\hspace{10 mm}

\underline{\textbf{VeriFinger SDK}}

\vspace{-5 mm}

\paragraph{}

\includegraphics{p1.png} Fingerprint identification for PC and Web solutions.

\section{Iris Verification}

\textbf{Strengths}

\begin{itemize}

\item Very high levels of accuracy

\item Each iris is a unique structure

\item Capable of reliable identification as well as verification

\end{itemize}

\vspace{12mm}

\newpage{}

\textbf{Weaknesses}

\begin{itemize}

\item Potentially low contrast pattern in dark irises. Also, some users don't accept eye-based technology

\item High cost capture devices or inconvenient devices

\item Not easy to use since light sensitivity of humans

\item Accuracy decreases when users wear eyeglass, Obscured by eyelashes, lenses/reflections

\item Any unusual lighting situations may affect the ability of the camera to acquire its subject

\end{itemize}

\subsection{Examples of Currently Available systems}

\underline{\textbf{VeriEye SDK}}

\paragraph{}

\includegraphics{p2.png} Eye iris identification for PC and Web solutions.

\paragraph{}\textbf{Robust eye iris detection. }Irises are detected even when the images have obstructions, visual noise and different levels of illumination. Lighting reflections, eyelids and eyelashes obstructions are eliminated. Images with narrowed eyelids or eyes that are gazing away are also accepted.

\section{Face Recognition}

\textbf{Strengths}

\begin{itemize}

\item Cheap hardware components and easy to be added to the existing computer systems

\item Can search against static images such as driver's license photographs

\item Operate without user cooperation

\end{itemize}

\textbf{Weaknesses}

\begin{itemize}

\item Changes in acquisition environment (outdoor instead of indoor) and physiological characteristics reduce matching accuracy

\item The accuracy is not satisfied

\item Cannot handle identical twins

\item Automatically locate and recognize a face from a general view point under different illumination conditions, facial expressions, and aging effects

\end{itemize}

\subsection{Examples of Currently Available systems}

\underline{\textbf{Face identification for video surveillance applications}}

\paragraph{}Surveillance VeriLook SDK is intended for developing biometric software that personal identification from live video streams of performing high resolution digital cameras. The SDK is based on VeriLook facial recognition technology and is used for passive biometrics - where passers no efforts are recognized. List of possible uses include law enforcement, security, control of attendance, counting visitors and other commercial applications.

The VeriLook Surveillance SDK allows creating applications for Microsoft Windows and Linux platforms.

\section{3D Hand Geometry}

\textbf{Strengths}

Compared with Fingerprint, hand geometry has:

\begin{itemize}

\item Less storage requirement

\item Faster

\item Lower cost

\item More acceptable

\end{itemize}

\textbf{Weaknesses}

\begin{itemize}

\item Human hand shape is not unique to each individual

\item Limited accuracy because of the simple features

\item Features not invariant over lifespan of an individual, especially during childhood

\item Limitations in dexterity (e.g., arthritis) of hand will lead to incorrect verification, as well as influences of rings and missing fingers

\item The physical size of a hand geometry-based system is large; cannot be used in embedded systems

\end{itemize}

\subsection{Examples of Currently Available systems}

\paragraph{}

Unlike fingerprints, no human hand is unique. One finger length for the purpose of inspection, the thickness, you can use the curvature for identification. Immigration and border controls, biometric invasion (for some types of cases, such as access control, a fingerprint) may be desirable to violate their privacy. In this situation, the system has the desired biological sufficient verification. The unique shape of the hand is not as it is the best. In addition, it is easy to collect the hand shape data. Skin friction is a good collection of fingerprint imaging required in the retina based recognition systems require special lighting. The other biometric hand geometry easily, that can be combined with the fingerprint. One (rare) system can be used to assume that is used to fingerprint and hand geometry identification (often), validation.

\section{Voice Identification}

\textbf{Strengths}

\begin{itemize}

\item Capable of leveraging telephony infrastructure

\item Effectively layers with other processes such as speech recognition and verbal passwords

\item Generally lacks the negative perceptions associated with other biometrics

\end{itemize}

\vspace{4mm}

\textbf{Weaknesses}

Potentially more susceptible to replay attacks than other biometrics

\begin{itemize}

\item Its accuracy is challenged by low-quality capture devices.

\item Cannot use in the noise environment

\item Success of voice-scan as a PC solution requires users to develop new habits, leading to the perception that voice verification is not user friendly.

\item Large size of template limits the number of potential applications

\item Require long training time.

\end{itemize}

\section{Signature Recognition}

\vspace{-2mm}

\textbf{Strengths}

\begin{itemize}

\item High user acceptance since it is similar to the existing pen based signature method

\item Resistant to imposters

\item Leverages existing processes

\item Perceived as non-invasive

\item Users can change signatures

\end{itemize}

\textbf{Weaknesses}

\begin{itemize}

\item Inconsistent signatures lead to increased error rates

\item Users are unaccustomed to signing on tablets

\item Has limited applications

\item Change over time

\item Professional forgers may be able to reproduce signatures

\item Some people cannot produce stable signatures, even successive impressions

\end{itemize}

\subsection{Examples of Currently Available systems}

\paragraph{}

\includegraphics{p3.png}

\\Camera-based acquisition system uses computer vision technology and estimation theory to track the location of the pen in terms of image. Validation algorithm compares the two-dimensional shape measurement using a translation invariant signature.

\section{Palmprint Authentication}

\textbf{Strengths}

\begin{itemize}

\item The palm provides a larger surface area compared with the fingerprint, so that more features can be extracted for personal identification.

\item \textbf{High accuracy:} Based on our database (>12,000 palms), it is close to iris, even higher than fingerprint.

\item \textbf{Real time:} Around one second for 1-to-up to 1,000 identification since <100 dpi resolution.

\item\textbf{Uniqueness:} more unique palmprint features than that of hand geometry

\item Non intrusive, high user acceptance

\end{itemize}

\vspace{-2mm}

\textbf{Weaknesses}

\begin{itemize}

\item Since it is a new biometric technology, there is not confidence from the public to use it

\item The size of the device is bulky, since it use optical components, similar to the hand geometry system

\end{itemize}

\subsection{Examples of Currently Available systems}

\paragraph{}

Palm consists of functional and reliable as the line of palm, ridge, the textures, such as before the feature extraction, we pre-treatment rate of interest on the image segment (ROI) space should be retrieved. We have to capture an image of the palm rest to get the fingers and wrist, such as The segmentation is an important step before extracting features from images of the palm area. Here, the

texture features are extracted. They have different line configurations principle wrinkles are thin and more irregular.

\chapter{Biometrics and Robotics}

\vspace{-8mm}

\section{Why Robotics}

\paragraph{}

Such a system is especially important for robotic vision where subject's cooperation or direct interaction of human and robot may not always be available. In some cases, a subject's involvement may cause severe threat to the biometric systems especially for identifying a suspect or a terrorist. In addition, an automatic personal identification system based solely on a particular biometric component such as fingerprint or face is often not able to meet the security satisfaction.

We need such a system where acquisition of biometric database and its verification could be developed without a subject's concern, and the system performance will satisfy the security requirement.

\section{Concept of Robotics Apply in Biometric Authentication}

\paragraph{}When thinking about one other biometric feature, the difference or the efficiency can be increased beyond the biological environment. Because some sense capturing the flexibility of a robotic environment is more efficient than a human. As an example, most human cannot be identified by the body features. But after converting it into digital media, this electronic information is a good set of biometric data separately many biometric objects.

If tracking of biometrics is used in the robotics environment is more difference in considering biometric authentication systems, because mobile robot environment can be in an office or a domestic or underwater swimming in the ocean. The rules for the keeping of biometrics have been changed by the environment used by the robotics. As an example consider mobile robot face license is used as biometric authentication to identify the man involved in the environment that is similar to the robot, it is difficult to face biometric light on speed, acceleration, angle the robot and the same things of the biometric object.

To solve this problem is the sensors and techniques used in mobile robot positioning concept were presented. With this concept, the error of the biometric data is calculated. The mobile robot positioning is included in these concepts.

\newpage{}

\subsection{Odometry}

\paragraph{}Odometry is the most widely used method for navigating mobile robot position and provides short-term good accuracy, inevitably to the unlimited accumulation of errors, the reduction in the efficiency of biometric data held by the sensors located in the mobile robot, because the basic idea of Odometry is the integration of incremental motion over time.

\subsection{Inertial Navigation}

\paragraph{}Inertial Navigation Systems, Inertial navigation uses gyroscopes and accelerometers to measure speed and acceleration, respectively. Measurements are taken internally by not taken off. These generators are usually not measured as part of objects associated with motion generators or consider gravity. By this method, the sensors captured the velocity and the statement of the biometric objects. Through these internal sensors data to drive with time, this time the position of the robot can

be calculated. Since the calculation of the angle of the object and the biometric sensor can be calculated. This sensor is a camera, thermo sensor, motion detector, sonar sensors.

Each type of biometric data capture divides to generate results depends on the distance and angle of the object and biometric devices. So calculating rate gyros most importing thing with robot motion. For these things internal sensors gyroscopes Indicate rate is a need for such a robot environment (given robot motion on the earth) for the responsibilities of special device called gyroscopes used.

\subsection{Gyroscopes}

\paragraph{}Gyroscopes are of particular importance for mobile robot positioning because they can help compensate for the main weakness of Odometry: in an Odometry -based positioning method. By this method a few small movements can be tracked. For this reason, it would be very useful as orientation errors can be detected and immediately corrected. If the benefits of this technology, internal navigation systems and robotics Pathfinder applications are implemented in the modern world.

\subsection{Global Positioning Systems (GPS) }

\paragraph{}GPS systems use the satellite-based positioning method in robotics area. This method is more flexible and efficient than considering previous technologies because previous cases only calculated the position of the parameters, but itself can calculate GPS others (biometric objects), so it is easy to race down the error of biometric data. It is more efficient when we used in environments that door robotics. The system consists of 24 satellites (including three spares), transmitting encoded RF signals. Using sophisticated methods Trilateration, ground receivers can calculate their position by measuring the travel time of the satellites 'radio signals, providing information about the satellites' temporary location to incorporate. Knowing the exact distance from the ground receiver to three satellites theoretically possible to calculate the receiver latitude, longitude and altitude.

Landmark Navigation: This method is more flexibility for indoor mobile robotics. Places are several characteristics that a robot can recognize the sensory input. These inputs are taken by cameras. Distance also calculated by the image processing. In general, landmarks have a fixed and known position, relative to a robot that can locate itself. Before a robot can use landmarks for navigation, the characteristics of the monuments known and stored in the memory of the robot. In biometric authentication concept of biometric data objects can be taken as landmarks. The challenge of this task is the biological objects can be moved so that the landmark could be false. So the robot cannot only depend on the Biometrics country brands.

Most techniques as such are used to navigate the general control robotics, but considering biometric authentication used in robotics applied to the fault of the actual output biometrics tracking components with the speed to calculate displacement and acceleration.

\section{Suggested Innovative Biometric systems}

\begin{enumerate}

\item The proposed system uses the extracted face and ear images to develop the respective feature spaces via the PCA algorithm called eigenfaces and eigenears, respectively. A new face and ear can be characterized by calculating the Euclidean distance between the classes of eigenfaces or eigenears and the new face or ear, respectively. Eighteen persons' faces and ears are employed for developing the databases of the eigenfaces and eigenears, and new faces and ears taken from various sessions of the same persons are employed for the identification. The proposed multimodal biometric system shows promising results than individual face or ear biometrics investigated in the experiments.

\vspace{-2mm}

\paragraph{}Suggested by M. Masudur Rahman, Seiji Ishikawa, Department of Mechanical and Control Engineering

There are three main steps involved in developing the proposed system: (a) extraction of images, pre-processing and normalization, (b) creating eigen databases for ears and faces, and (c) verification.

\item A biometrics authentication device, which uses blood vessel images of a human body to perform individual authentication, performs verification processing according to a detected body temperature. A temperature sensor, which detects the temperature of a body, is provided in a blood vessel image capture device. The temperature at the time of registration of a blood vessel image is stored together with the blood vessel image, and the temperature of the body at the time of use (at the time of verification) is detected; the temperature difference at the time of registration and at the time of use is reflected in the verification processing, and when there is a verification error, the cause of the verification error is judged from the temperatures at the time of registration and at the time of use. Whether a verification error is due to temperature can be easily distinguished.

\item Brain and heart patterns are the new areas of research for the biometric developers to make their use in full-proof user authentication process. Over the years of existence of biometric technology, it has been proved that fingerprints, face patterns and even voice patterns can be forged by the criminals. However, faking the brain and heart patterns, belonging to an individual, is going to be next to impossible.

\vspace{14mm}

\\

\textbf{Technologies Already Existing}

\\Most of you are familiar with electrocardiogram (ECG) that record heart rhythms and electroencephalogram (EEG) that help the medical experts to analyze the brain patterns. Needless to say, the use of both these technologies has made it possible for the medical experts to discover the disorders in two important human body parts. Both of these technologies can significantly contribute towards the development of improved biometric systems.

What will be required to move further is the integration of these technologies with biometric analysis to develop the capable biometric systems. Rest, the process would be same as that

used for enrolling, storing and verifying the biometric samples using fingerprint scanners, face scanners and so on.

\vspace{5mm}

\\

\textbf{Possible Areas of Application}

\\When developed, the brain pattern recognition or heart beat recognition using biometrics can prove to be useful in various biometric applications. Here are some of the examples:

\begin{itemize}

\item For authentication at the airport entry points that definitely require a sophisticated authentication technology.

\item Border crossing points to avoid illegal exchange of people through international boundaries.

\item Banks, labs and many other security-sensitive areas can be facilitated with the development of the two new biometric technologies.

\item European biometric researchers are working on the projects to deliver some results in the nearby future. Biometrics is expected to take a giant leap with it.

\end{itemize}

\item Speech Recognition by Humanoid Robot in Real Environment

\\\textbf{Research Background}

\\ Since the announcement of Humanoid Robot P2 by Honda Motor Co., Ltd. in 1996, R and D works on the humanoid robot have been increased energetically not only in Japan but also over the world. In the technological strategic map for robotics drafted by the Ministry of Economy, Trade and Industry (METI), it is planned to ensure practical use of robots supporting human labor in the living environment by 2025, such as supporting household works, self-reliance support, assistance and nursing care for aged persons.

While previous R and D efforts for humanoid robot technology have been focused on robot locomotion aiming at safe and stable walks and behaviors, as well as robot vision, little have been done in full-scale technological development of hearing function of robot, which plays an important role in establishing natural communications between humans and robots.

In the living environment, where practical use of next generation robots is expected, direct human-robot interaction through voice channel is growing to one of key perceptive functions of robot.

\newpage{}

\item Object recognition for robotics and computer vision

\\SentiSight is intended for developers who want to use computer vision-based object recognition in their applications. It enables manual and fully automatic object learning, and searching for learned objects in images from almost any camera, webcam, still picture or live video in an easy, yet versatile, way.

SentiSight is available as a software development kit that allows the development of object recognition systems for Microsoft Windows or Linux platforms.

\end{enumerate}

\bibliographystyle{plain}

\bibliography{07000731.bib}

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