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Here we use three diï¬€erent concepts to track the human. At ï¬rst we use the mobile tracking system GPS and GPRS tracking system. After that when the device is lost or destroyed then we search the human with the FaceVACS-video system. FaceVACS-VideoScan uses world-leading face recognition technology to detect and identify persons of interest while computing demographic and behavioral data, making it the only face-recognition-based video screening technology on the market that can support security staï¬€ and operations management simultaneously. Face recognition technology, like any biometric application, cannot provide 100 % recognition accuracy. Analyzes video streams and detects faces. Key words: FaceVAC,GPS ,GPRS,Face Rocognition.
In virtual space technology, a tracking system is generally a system capable of rendering virtual space to a human observer while tracking the observer's body coordinates. For instance, in dynamic virtual auditory space simulations, a real-time head tracker provides feedback to the central processor, allowing for selection of appropriate head-related transfer functions at the estimated current position of the observer relative to the environment. In the present day it is one of the most common topic cause for searching the man or any other people who did the violence or some where the people lost their destination. The tracking is hard for many reason that is if we ï¬nd the human, there are many things at the same time. Sometimes it will be hard work for reach the destination. Sometimes the devices may not work well. In previous day a lot of work on the tracking system .Some of them are successful and some are not. Some of them missing the frame. Some of them can not recognize the original person in the time.GPS or GPRS are depending on the mobile device. Here also a problem that, if the human lost their device then what will be the solution. There are also be the problem with infrared detectors. In this paper want to make a device or system that will track the human if the device is lost or destroyed.
Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. The Kalman ï¬lter (KF) has commonly
been used for estimation and prediction of the target position in succeeding frames. Experimental results show the suggested approach is very eï¬€ective and reliable for estimating and tracking moving objects. Another development is a mobile assistive companion robot by combining a vision sensor and a laser range sensor to track and follow a target person. Most of tracking systems with multiple networked sensors need any host computers for collecting and integrating measurements from all sensors . The continual miniaturization of the GPS chipset means that receivers can take the form of wristwatches, mini mobiles and bracelets, with the ability to pinpoint the longitude and latitude of a subject. Moving blobs are detected based on adaptive background modeling. A shape based multi-view human detection system is used to ï¬nd humans in moving regions. There are also GPS and GPRS Based Cost Eï¬€ective Human Tracking System Using Mobile Phones. There are also another work with the infrared detectors.
Details about the FaceVACS-Videoscan
In this system at ï¬rst we use the GPS or GPRS tracking system. Then when the human being destroyed the device then we can use the system which is named by the FaceVACS-VideoScan or image. For using the system we must be used a diï¬€erent types of video camera which has diï¬€erent types of technical facts. FaceVACS-VideoScan uses world-leading face recognition technology to detect and identify persons of interest while computing demographic and behavioral data. 3.1 Face Recognition Concepts
There are three things in the face recognition concepts. They are- i)Enrollment, ii) Veriï¬cation, and iii) Identiï¬cation. 3.1.1 Enrollment
An initial feature set is constructed from the relevant physical traits of the user.
Fig. 1. Enrollment.
3.1.2 Veriï¬cation Extracted feature set from each person is compared with the enrollment feature set. If the resulting score value is above a predeï¬ned threshold, the user is considered to be authenticated.
Fig. 2. Enrollment.
3.1.3 Identiï¬cation In contrast to the veriï¬cation use case, with identiï¬cation the (claimed) identity of the user is not known in advance, but shall be determined based on sample images of the user's face and a set (population) of feature sets with known identities. 3.1.4 FAR, FRR and EER FAR (False Acceptance Rate) is the probability that a sample falsely matches the presented FIR. FRR (False Rejection Rate) is the probability that a sample of the right person is falsely rejected. The value of FAR and FRR at the point where the plots cross is called the Equal Error Rate (EER) . 3.2 FaceVACS Architecture
In FaceVacs architecture it is depend on may things such as: face Localization, Eye Localization, Image Quality Check, Normalization, Preprocessing, Feature Extraction, Comparison, Construction of the Reference set, Comparison. 3.3 FaceVACS Technology
Here incorporates B7T8 facial recognition algorithm. Here algorithm is robust against. Typical gesture changes. Pose(-/+ 15 degree deviation from frontal image. Partial face occlusion. Glasses, beard and hairstyle changes, lighting variations . Showed in ï¬g 3. 3.4 Requirements of using FaceVACS Technology
3.4.1 Minimal still image requirements for facial recognition Here we need sharp image. Inter-pupil spacing larger 32 pixels. At least 64 grayscale values within the face area.
Fig. 3. Face recognition by using algorithm.
Minimal requirement for optimal facial recognition
ISO 19794-5 compliant Token Frontal or Full Frontal image. 3.4.3 Biometric characteristics
Conï¬gurable score threshold. With this system we can also use the video. 3.5 Console
In FaceVACS-VideoScan , console provides the central view to the systems video analytics capabilities. Here we can monitoring timeline events, persons and statistics. Allows user to create or modify image galleries. Here can be supply the details the persons. Here also be a dashboard. 3.6 System Architecture
Distributed architecture of video servers, cluster controllers, computing nodes, and database server. Highly scalable and robust against hardware failure. Alarm dispatcher can send events to mobile devices. 3.7 Cluster Controller
Computes visits of persons within zones from incoming face streams through biometric analysis. Compares incoming face streams to multiple galleries. Includes pre-deï¬ned catalogs of event detectors and Statistics. Here is integration API. 3.8 System Components
In this system multiple components can coexist on single computer. One or multiple video servers can be operate video streams. Co-ordinates all the system components and performs main biometric operation. Here the system conï¬gurable as matcher or encoder. In this systems, the mobile receives notiï¬cation from cluster controller. Operational database used by cluster controller .
Fig. 4. 3D face recognition.
Image format support such as- JPG, JPG 2000, PNG, BMP, PPM, PGM. Here supports diï¬€erent operating system such as- Windows Server 2008 R2, Windows Professional and higher, Suse Linux enterprise Server 11, 64bit version. Here the operational database is PostgreSQL, which can be synchronized with external database. Cognitec provides sizing and hardware recommendations according to the application scenario.
Here we use the concept of FaceVAC, GPS and GPRs tracking system. We have so many limitation to do this paper. We want to track the terrorist or to ï¬nd those people they lost their desire destination. Here we ï¬rst use the mobile tracking system by using GPS or GPRs. But when the device will be destroyed ,then we use FaceVAC system. By using this system we need to use very good quality of camera. We also have the shortness of proper devices.There are many environmental causes which we can done in this paper. Also if this camera will be destroyed then what will be the other component, it will be ï¬nd in near future.