Paper Info
Title
Sparse Representation for Video-Based Face Recognition
Abstract
In this paper we address for the first time, the problem of video-based face recognition in the context of sparse representation classification (SRC). The SRC classification using still face images, has recently emerged as a new paradigm in the research of view-based face recognition. In this research we extend the SRC algorithm for the problem of temporal face recognition. Extensive identification and verification experiments were conducted using the VidTIMIT database [1,2]. Comparative analysis with state-of-the-art Scale Invariant Feature Transform (SIFT) based recognition was also performed. The SRC algorithm achieved 94.45% recognition accuracy which was found comparable to 93.83% results for the SIFT based approach. Verification experiments yielded 1.30% Equal Error Rate (EER) for the SRC which outperformed the SIFT approach by a margin of 0.5%. Finally the two classifiers were fused using the weighted sum rule. The fusion results consistently outperformed the individual experts for identification, verification and rank-profile evaluation protocols.
Year
DOI
Venue
2009
10.1007/978-3-642-01793-3_23
ICB
Keywords
Field
DocType
temporal face recognition,view-based face recognition,recognition accuracy,video-based face recognition,sparse representation classification,sift approach,sparse representation,src classification,extensive identification,src algorithm,verification experiment,comparative analysis,scale invariant feature transform,face recognition
Scale-invariant feature transform,Computer vision,Facial recognition system,3D single-object recognition,Pattern recognition,Computer science,Sparse approximation,Word error rate,Speech recognition,Independent component analysis,Artificial intelligence
Conference
Volume
ISSN
Citations 
5558
0302-9743
3
PageRank 
References 
Authors
0.42
16
3
Name
Order
Citations
PageRank
Imran Naseem114213.51
Roberto Togneri281448.33
M. Bennamoun33197167.23