Authors:
Vijayan Asari
and
Almabrok E. Essa
Affiliation:
University of Dayton, United States
Keyword(s):
Volumetric Directional Pattern, Key Frame Extraction, Video to Video Face Recognition.
Abstract:
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 analysing all the frames in
the video. The perfo
rmance evaluation of the proposed VDP algorithm is conducted on a publicly available
database (YouTube celebrities’ dataset) and observed promising recognition rates.
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