Local Binary Pattern based Resolution Variation Video-based Face Recognition

Video-based face recognition is a very challenging problem as there is a variation in resolution, illumination, pose, facial expressions and occlusion. In this paper, we have presented an approach for resolution variation video-based face recognition system using the combination of local binary pattern (LBP), principal component analysis (PCA) and feed forward neural network (FFNN). We used, standard as well as created database. The main purpose of this paper is to evaluate the performance of the system. To the best of our knowledge this is the first work addressing the issue of resolution variation for video-based face recognition with this approach. We have experimented with three different video face databases (Created database, NRC_IIT & HONDA/UCSD) and compared with benchmark methods. Experimental results show that our system achieves better performance than other video-based face recognition algorithms on challenging resolution variation video face databases and thus advancing the state-of-the-art. Key-Words: Video-based face recognition, Local Binary Pattern, Principal Component Analysis, Feed forward Neural Network.