Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are used to match with all the videos in the database. To make the approach computationally simple and easy to extend, key-frame extraction method is employed.

Therefore, only the frames which contain important information of the video can be used for further processing instead of analyzing all the frames in the video. The performance evaluation of the proposed VDP algorithm is conducted on a publicly available database (YouTube celebrities’ dataset) and observed promising recognition rates.