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Journal Article Facial Attribute Recognition by Recurrent Learning With Visual Fixation
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Authors
Jinhyeok Jang, Hyunjoong Cho, Jaehong Kim, Jaeyeon Lee, Seungjoon Yang
Issue Date
2019-02
Citation
IEEE Transactions on Cybernetics, v.49, no.2, pp.616-625
ISSN
2168-2267
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/TCYB.2017.2782661
Abstract
This paper presents a recurrent learning-based facial attribute recognition method that mimics human observers' visual fixation. The concentrated views of a human observer while focusing and exploring parts of a facial image over time are generated and fed into a recurrent network. The network makes a decision concerning facial attributes based on the features gleaned from the observer's visual fixations. Experiments on facial expression, gender, and age datasets show that applying visual fixation to recurrent networks improves recognition rates significantly. The proposed method not only outperforms state-of-the-art recognition methods based on static facial features, but also those based on dynamic facial features.
KSP Keywords
Facial attribute recognition, Facial image, Learning-based, Over time, Recognition method, Recognition rate, Recurrent network, facial expression, facial features, state-of-The-Art