An optical flow-based approach to robust face recognition under expression variations.
Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize faces with expressions under the restriction of one single training sample per class. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intraperson optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.
Hsieh
Chao-Kuei
CK
Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC. d903915@oz.nthu.edu.tw
Lai
Shang-Hong
SH
Chen
Yung-Chang
YC
D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*