Real-Time Face Recognition Based on Optical Flow and Histogram Equalization

Face recognition is one of the intensive areas of research in computer vision and pattern recognition but many of which are focused on recognition of faces under varying facial expressions and pose variation. A constrained optical flow algorithm discussed in this paper, recognizes facial images involving various expressions based on motion vector computation. In this paper, an optical flow computation algorithm which computes the frames of varying facial gestures, and integrating with synthesized image in a probabilistic environment has been proposed. Also Histogram Equalization technique has been used to overcome the effect of illuminations while capturing the input data using camera devices. It also enhances the contrast of the image for better processing. The experimental results confirm that the proposed face recognition system is more robust and recognizes the facial images under varying expressions and pose variations more accurately.