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• W2950902768 abstract "An excellent face recognition for a surveillance camera system requires remarkable and robust face descriptor. Binary gradient pattern (BGP) descriptor is one of the ideal descriptors for facial feature extraction. However, exploiting local features merely from smaller region or microstructure does not capture a complete facial feature. In this paper, an extended binary gradient pattern (eBGP) is proposed to capture both micro- and macrostructure information of a local region to boost up the descriptor performance and discriminative power. Two topologies, the patch-based and circular-based topologies, are incorporated with the eBGP to test its robustness against illumination, image quality, and uncontrolled capture conditions using the SCface database. Experimental results show that the fusion between micro- and macrostructure information significantly boosts up the descriptor performance. It also illustrates that the proposed eBGP descriptor outperforms the conventional BGP on both the patch-based topology and the circular-based topology. Furthermore, a fusion of information from two different image types, orientational image gradient magnitude (OIGM) and grayscale image, attained better performance than using OIGM image only. The overall results indicate that the proposed eBGP descriptor improves the recognition performance with respect to the baseline BGP descriptor." @default.
• W2950902768 created "2019-06-27" @default.
• W2950902768 creator A5046243503 @default.
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• W2950902768 date "2019-12-18" @default.
• W2950902768 modified "2023-10-14" @default.
• W2950902768 title "Extended Binary Gradient Pattern (eBGP): A Micro- and Macrostructure-Based Binary Gradient Pattern for Face Recognition in Video Surveillance Area" @default.
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• W2950902768 doi "https://doi.org/10.5772/intechopen.86473" @default.
• W2950902768 hasPublicationYear "2019" @default.
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