Face identification methodologies in videos

Face recognition is an evolving area, changing and improving constantly. Face recognition through video has recently drawn lots of attention from both the research community and industry and is beginning to be applied in a variety of domains, predominantly for security. Searching image in dictionary which consists of large number of video frames is a challenging task. Various techniques have been introduced in the past few years which use the technique to process video frames in serial manner which results to lack of performance degradation. We proposed method with which recognition can now be carried out to process video frames in a parallel manner and with this parallel processing of video frame's rank list a substantial amount of time would help to increase the efficiency and decrease the execution time. An approach is proposed to increase the efficiency of the system in which video frames are divided into four parts to increase the speed of the system. The images obtained are then ranked, clustered and re-ranked to get the matching images of the two videos. The efficacy of proposed system can be evaluated using some standard database such as You Tube or MBGC v2 database and shows better results as compared to existing methods. The proposed approach works for each video frames in a sequence. For a sequence of frames, the likelihoods are summed, and compared at the end of the sequence, taking the maximum likelihood training model as the correct result. With this approach we illustrate that our technique is efficient and performs extensively better than many viable video-based face recognition algorithms.