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Face Region Based Conversational Video Coding Bing Xiong, Xiaojiu Fan, Ce Zhu, Senior Member, IEEE, Xuan Jing, and Qiang Peng
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Outline Introduction Motion-based sub-image rejection for face detection Face Region Based Bit Allocation in H.264/AVC Experimental Results and Discussions
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Introduction Human visual system pays more attention to region of interest(ROI). Bit allocation How to find the proper face region Liu et al. [9] attempted to alleviate the artifact by smoothening the ROI coding priority mask with a mean filter.
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Face detection Pyramid Structure in Face Detection Efficient Motion Detection Using Block Mean Difference Sub-Image Rejection Using Motion Features Verification With Facial Color Statistics
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Pyramid Structure in Face Detection
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Increase S will decrease N
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Efficient Motion Detection Using Block Mean Difference Motion detection using the mean difference
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Efficient Motion Detection Using Block Mean Difference
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Sub-Image Rejection Using Motion Features
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Thresholds are empirically set to 10% set to 10%
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Sub-Image Rejection Using Motion Features
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Remaining sub-images will be classified by Viola’s face detection method[12] Integral image Ada boost Cascade classifier
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Verification With Facial Color Statistics skin-tone based face verification scheme can be found in [14]
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Face Region Based Bit Allocation in H.264/AVC Face Contour Extraction Facial Feature Priority Based Bit Allocation Face ROI Based Video Coding Architecture
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Face Contour Extraction The snake algorithm converges slowly Use “normal line search” on initial contour
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Facial Feature Priority Based Bit Allocation T[i] remaining bits before coding ith MB QP[i] function introduced in [22]
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Facial Feature Priority Based Bit Allocation
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Face ROI Based Video Coding Architecture
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Environment 100 frames,first frame intra-frame (I frame) and the rest frames as inter-frames (P frames) H.264/AVC codec JM9.8 2.66 GHz Intel(R) Core(TM)2 Duo CPU and 2G memory running Microsoft Windows XP
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Experimental Results and Discussions
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