Abstract-Biometric authentication of a person is highly challenging and complex problem. A significant research effort has gone into this area and a number of research works were published, but still there is an immense shortage of accurate and robust methods and techniques. In this paper we survey several important research works published in this area and we found our new technology to identify a person using multimodal physiological and behavioural biometrics. For our first stage of experimental evaluation, we used side face and gait for our experiments and we achieved around 100% recognition rate.

Face detection, recognition and gender estimation are one of the most significant research areas in computer vision, not only because of the challenging nature of faces as an object but also due to the countless applications that require the application of face detection, tracking and recognition. Although many significant types of research on face detection, recognition and gender estimation problems have done in the last few years separately, there is no particular research on face detection, recognition and gender estimation together from a real-time video for person identification. So, we feel that these types of significant research are still needed to work. The main contributions of our paper are divided into three parts, namely face detection, recognition and gender estimation for person identification. In our research work, we use Local Binary pattern Histogram (LBPH) method and Convolution Neural Network (CNN) to extract the facial features of face images whose computational complexity is very low. By calculating the Local Binary Patterns Histogram (LBPH) neighborhood pixels and Convolution levels, we extract effective facial feature to realize face recognition and gender estimation. We show the experimental results using these methods to recognize face and gender for person identification. CNN increase the calculating speed of testing real-time video and also improve the recognition rate. By using LPBH, we get 63% accuracy on average where CNN gives 99.88% training accuracy for face recognition-1 and 96.88% accurate for gender estimation-1 and 100% training accuracy for face recognition-2 and 93.38% training accuracy for gender estimation-2. However, Convolution Neural Networks (CNN) learns fast and predict efficiently.

No abstract

No abstract