Robust face recognition under illumination and facial expression variations

Ching Liang Lu, Luo Wei Tsai, Yuan Kai Wang, Kuo Chin Fan

研究成果: 書貢獻/報告類型會議論文篇章同行評審

7 引文 斯高帕斯(Scopus)

摘要

Illumination and expression variations are still a challenging problem in face recognition. In this work, we present an efficient face recognition method which can solve the above two problems with single training sample. At first, the effect of the lighting variation is effectively eliminated by the Mutil-Scale Retinex algorithm. The Active Appearance Model is adopted to extract the facial block feature to establish the component-based face recognition system. Different from other methods which construct the various classifiers corresponding to the specific facial expression, the proposed method decreases the weights of some dominated facial features which are affected by the severe facial expression. By learning a block weighting support vector machine, the component based approach is achieved. The proposed algorithm has two advantages: (1) only single one face training image is needed to train the classifier; (2) by using the facial block features with lower data dimensions, the proposed system is more computational efficiency. In particular, the proposed method achieves 97.94% face recognition accuracy when only using one training sample on the Yale B database. Experimental results demonstrate that the proposed method has reliable recognition rate when face images are under illumination and facial expression variations.

原文???core.languages.en_GB???
主出版物標題2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
頁面3257-3263
頁數7
DOIs
出版狀態已出版 - 2010
事件2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
持續時間: 11 7月 201014 7月 2010

出版系列

名字2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
6

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???event.eventtypes.event.conference???2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
國家/地區China
城市Qingdao
期間11/07/1014/07/10

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