6 March 2015 Robust textural features for real time face recognition
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Automatic face recognition in real life environment is challenged by various issues such as the object motion, lighting conditions, poses and expressions. In this paper, we present the development of a system based on a refined Enhanced Local Binary Pattern (ELBP) feature set and a Support Vector Machine (SVM) classifier to perform face recognition in a real life environment. Instead of counting the number of 1's in ELBP, we use the 8-bit code of the thresholded data as per the ELBP rule, and then binarize the image with a predefined threshold value, removing the small connections on the binarized image. The proposed system is currently trained with several people's face images obtained from video sequences captured by a surveillance camera. One test set contains the disjoint images of the trained people's faces to test the accuracy and the second test set contains the images of non-trained people's faces to test the percentage of the false positives. The recognition rate among 570 images of 9 trained faces is around 94%, and the false positive rate with 2600 images of 34 non-trained faces is around 1%. Research work is progressing for the recognition of partially occluded faces as well. An appropriate weighting strategy will be applied to the different parts of the face area to achieve a better performance.
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Chen Cui, Chen Cui, Vijayan K. Asari, Vijayan K. Asari, Andrew D. Braun, Andrew D. Braun, } "Robust textural features for real time face recognition", Proc. SPIE 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015, 940806 (6 March 2015); doi: 10.1117/12.2083400; https://doi.org/10.1117/12.2083400

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