Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition

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This paper proposes a new video face recognition (FR) method that is designed for significantly improving FR via adaptive fusion of multiple face features (belonging to the same subject) acquired from a face sequence of video frames. In this paper, we derive an upper bound for recognition error arising from the proposed weighted feature fusion to justify theoretically its effectiveness for recognition from videos. In addition, in order to compute the optimal weights of face features to be fused, we develop a novel weight determination solution based on fuzzy membership function and quality measurement for face images. Using four public video databases, the effectiveness of the proposed method has been successfully evaluated under the conditions that are similar to those in real-world video FR applications. Furthermore, our method is simple and straightforward to implement.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2012-08
Language
English
Article Type
Article
Keywords

ALGORITHMS; MANIFOLDS; IMAGES

Citation

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, v.42, no.4, pp.1270 - 1282

ISSN
1083-4419
DOI
10.1109/TSMCB.2012.2185693
URI
http://hdl.handle.net/10203/102369
Appears in Collection
EE-Journal Papers(저널논문)
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