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• W2266804582 abstract "In this paper, we propose a new approach for face representation and recognition based on adaptively weighted sub-Gabor array (AWSGA) when only one sample image per person is available. Instead of using holistic representation of face images which is not effective under different facial expressions, varying lighting conditions and partial occlusions, the proposed algorithm utilizes a local Gabor array to represent faces partitioned into sub-patterns. Especially, in order to perform matching in the sense of the richness of identity information rather than the size of a local area and to handle the partial occlusion problem, the proposed method employs an adaptively weighting scheme to weight the sub-Gabor features extracted from local areas based on the importance of the information they contain and their similarities to the corresponding local areas in the general face image. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal condition, different illumination condition, different facial expression and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The promising experimental results indicate that the proposed method can greatly improve the recognition rates under different conditions." @default.
• W2266804582 created "2016-06-24" @default.
• W2266804582 creator A5063666308 @default.
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• W2266804582 date "2010-01-01" @default.
• W2266804582 modified "2023-09-24" @default.
• W2266804582 title "ADAPTIVELY WEIGHTED SUB-GABOR ARRAY FOR SINGLE MODEL-BASED HUMAN FACE RECOGNITION" @default.
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