Technique Description Performance Evaluation Matrices

Biometrics technology aims at utilizing major and distinctive characteristics such as behavioral or biological, for the sake of positively indentifying people. With the help of a combination of hardware and specific identifying sets of rules, a basic human attribute, automated biometric recognition mimics to distinguish and categorize other people as individual and unique. But the challenges surrounding biometrics are great as well.

In the present contemporary era, facial recognition technologies are being installed by the companies in an extensive sense that surely reflects a continuum of growing hi-tech superiority and complexity. At the most ordinary level, facial detection is done by this technology which means that a photo is just detected and located for a face ("Facing Facts: Best Practices for Common Uses of Facial Recognition Technologies," 2012).

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

Gait recognition aims to identify people by the way they walk. Therefore Several Parameters proposed for Gait Recognition previously but there is always a need for better parameters to improve recognition. The existing Gait Recognition Technique does not consider the distance between hands during movement. Proposed recognition system implement an Enhanced Gait Recognition Technique which is based on model based approach that is more accurate and assures quality of result as it considers more parameters than previously considered like distance between hands during movement. Proposed Enhanced Gait Recognition Technique show enhanced BPNN result and the results will be obtained using BPNN, SVM, LDA and MDA

Face recognition is another biometric technology. Face recognition uses the same technology that iris recognition uses. For face recognition, a camera takes several images of a person to find out who it is. Face recognition differs from all of the other biometric technologies because the person that is in the picture does not have to cooperate with the process. In all of the other technologies it requires the people to actively participate in the process. In face recognition, the image can be taken without the person even knowing that it took place.

Through this routine of advanced technology analysis, it has been established to increase the results and have hastened the procedure of identifying suspects of crimes. Facial recognition is also necessary for public involvement and observation as it also aids law enforcement officials to more easily zone in on possible suspects of a crimes being caught. With the use of facial recognition, it constantly has been proven quite an effective method with the incorporation of this technique.

In this way the biometric data of the user through sensors pick up and then passed to the feature extractor to generate a template.

Face recognition has attracted great attention in recent years. An important application of face recognition is to assist law enforcement. For example, Uhl and lobo [1] proposed the first automatic retrieval of photos using a query sketch based on face recognition algorithm and more recently Klare and jain [2] suggests an effective method of matching sketch photo using many technique such as local descriptors similarities algorithm .Once this forensic sketch is ready, it is disseminated to law enforcement officers and media outlets hoping that someone might be knowing the suspect. The first approaches developed by Tang and Wang (2002, 2003, 2004) [3] use global linear transformations, based on eigenface method to convert

1966: The First Facial recognition systemdeveloped in 1966 helped administrator identify features on photographs like ears, mouth and nose to calculate ratios and distances to a reference point.

Bruce and Young’s theory of recognition tells us that human’s extract several kinds of information from faces; and that there are eight different components of such information. Such as structural encoding, expression analysis, facial speech analysis, directed visual processing, face recognition nodes,

Automatic face recognition has always been a major focus of research for a few decades, because of numerous practical applications where human identification is needed. Compared to other methods of identification (such as fingerprints, voice, footprint, or retina), face recognition has the advantage of its non-invasive and user friendly nature. Face images can be captured from a distance without interacting with the person, which is particularly beneficial for security and surveillance purposes. Furthermore, extra personal information, like gender, face expressions or age, can be obtained by further analyzing recognition results. Nowadays, face recognition technology has been widely applied to public security, person verification, Internet

In facial recognition a face model was created by Bruce and Young in 1986 and is one of the most influential face models to be created and is still used. It comprises of eight components: structural encoding, expression analysis, facial speech analysis, directed visual processing, face recognition nodes, person identity nodes, name generation and cognitive system. According to their face recognition model various types of information are acquired from faces. When recognising a familiar face, the components used depend largely on structural encoding, face recognition units, person identity nodes and name generation. In comparison with an unfamiliar face, the components used depend largely on structural encoding, expression analysis, facial speech analysis and directed visual processing. However, as the face model created by Bruce and Young (1986) was quite complex, a simpler version was created by Duchaine and Nakayama (2006) which provided four stages: face detection, structural encoding, face memory and emotion/gender etc.

Biometric technologies are getting better and finely tuned. The rate of false readings and errors has sharply fallen. However it still requires careful consideration and planning to implement a biometric identification system. They are most costly and complicated to implement as compared with other authentication systems. A proper evaluation of the system is important before purchasing any biometric system. A thorough risk analysis is necessary. In many cases biometrics may be overkill. Biometrics must be used if there is high level of risk involved. Customer acceptance is also important when logging on to company websites. Home users might not be ready to install biometrics on home computers for online banking.

In this paper, we propose an automated system that takes the attendance of students for classroom lectures. Our system takes the attendance using face detection and face recognition. We have constructed the lecture attendance system based on face recognition, and is applied on the system of classroom lecture. This paper first reviews the related works in the field of attendance management and face recognition. The face is the identity of a person. The methods to exploit this physical feature have seen a great change since the advent of image processing techniques. The attendance is taken in every schools, colleges and library. Traditional approach for attendance is professor calls student name & record attendance. It takes some time to record attendance. Suppose duration of class of one subject is about 50 minutes & to record attendance takes 5 to 10 minutes. For each lecture this is wastage of time. To avoid these losses, we are about use automatic process which is based on image processing. we are using face detection & face recognition system. The camera will be connected to the Raspberry pi module. The student database is collected. The database includes name of the students, there images & roll number. Thus with the help of this system, time will be saved. it is so convenient to record attendance. We can take attendance on any time.