Fusing the facial temporal information in videos for face recognition

Face recognition is a challenging and innovative research topic in the present sophisticated world of visual technology. In most of the existing approaches, the face recognition from the still images is affected by intra-personal variations such as pose, illumination and expression which degrade the performance. This study proposes a novel approach for video-based face recognition due to the availability of large intra-personal variations. The feature vector based on the normalised semi-local binary patterns is obtained for the face region. Each frame is matched with the signature of the faces in the database and a rank list is formed. Each ranked list is clustered and its reliability is analysed for re-ranking. To characterise an individual in a video, multiple re-ranked lists across the multiple video frames are fused to form a video signature. This video signature embeds diverse intra-personal and temporal variations, which facilitates in matching two videos with large variations. For matching two videos, their video signatures are compared using Kendall-Tau distance. The developed methods are deployed on the YouTube and ChokePoint videos, and they exhibit significant performance improvement owing to their approach when compared with the existing techniques.