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학술지 Facial Attribute Recognition by Recurrent Learning With Visual Fixation
Cited 1 time in scopus
저자
장진혁, 조현중, 김재홍, 이재연, 양승준
발행일
201902
출처
IEEE Transactions on Cybernetics, v.49 no.2, pp.616-625
ISSN
2168-2267
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TCYB.2017.2782661Y
협약과제
17HS6500, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
초록
© 2013 IEEE. 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.
키워드
Age detection, facial expression recognition, gender detection, recurrent learning, visual fixation
KSP 제안 키워드
Facial Expression Recognition(FER), Facial attribute recognition, Facial image, Gender detection, Learning-based, Over time, Recognition method, Recognition rate, Recurrent network, age detection, facial features