Attended Relation Feature Representation of Facial Dynamics for Facial Authentication

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In psychology, it is known that facial dynamics benefit the perception of identity. This paper proposes a novel deep network framework to capture identity information from facial dynamics and their relations. In the proposed method, facial dynamics occurred from smile expression are analyzed and utilized for facial authentication. Detailed changes in the local regions of a face such as wrinkles and dimples are encoded in the facial dynamic feature representation. The latent relationships of the facial dynamic features are learned by the facial dynamic relational network. In the facial dynamic relational network, the relation features of the facial dynamic are encoded and the relational importance is encoded based on the relation features. As a result, the proposed method has more attention on the important relation features in facial authentication. Through comprehensive and comparative experiments, the effectiveness of the proposed method has been verified in facial authentication.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-07
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v.14, no.7, pp.1768 - 1778

ISSN
1556-6013
DOI
10.1109/TIFS.2018.2885276
URI
http://hdl.handle.net/10203/260811
Appears in Collection
EE-Journal Papers(저널논문)
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