HOG-CNN Based Real Time Face Recognition

Face recognition presents a challenging problem in the field of image analysis and computer vision. Face recognition is a biometric system used to identify or verify a person from a digital image mostly used in security and surveillance purpose. Great success has been achieved recently on general object recognition by means of deep neural networks. Thus we are inspired to inspect the effectiveness of deep neural network on face recognition. This paper presents a deep neural network architecture referred as HOG-CNN for face recognition. The goal of this paper is face recognition in real time i.e. using webcam, from a photograph or from a set of faces tracked in a video. In recognition phase we measured the distance between the landmarks and compared the test image with different known encoded image landmarks. Face Recognition involves extracting features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose.