Robust face verification via Bayesian sparse representation

Chien Yao Wang, Seksan Mathulaprangsan, Bo Wei Chen, Yu Hao Chin, Jing Jia Shiu, Yu San Lin, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit (OMP) algorithm, the proposed system that utilized Bayesian method to model the optimization problem of sparse representation can reduce the uncertainty of observed signals and expand the modeling ability of dictionaries by using variance. The experimental results show that the proposed extended dictionary can enhance the sparsity. Furthermore, it can improve accuracy rates of face identification and residues of reconstruction.

原文???core.languages.en_GB???
主出版物標題2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9789881476821
DOIs
出版狀態已出版 - 17 1月 2017
事件2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
持續時間: 13 12月 201616 12月 2016

出版系列

名字2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

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???event.eventtypes.event.conference???2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
國家/地區Korea, Republic of
城市Jeju
期間13/12/1616/12/16

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