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Face annotation and expression analysis method
Author: YaoHaiYan
Tutor: ShiDongCheng
School: Changchun University of
Course: Signal and Information Processing
Keywords: Face annotation Expression Recognition Feature Extraction BU3DFE database Gabor wavelet transform
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 51
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Abstract
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The expression of the face is formed by the muscles of the face of a movement, a change in the face of the manifestations of human emotions . Changes in the facial expression of a lot of functionality , capable of displaying human mental activity , able to adjust the social behavior of the people , able to show that the pain sensation . Very valuable for automatic analysis and recognition of facial expressions in social psychology and related disciplines . Quite difficult due to the difference in the face of non-rigid nature of the interaction between personality , race , and other aspects , the automatic analysis and recognition of facial expression . Firstly, in the summary and general face annotation algorithm based on the proposed effective algorithm to extract the face and facial feature . This algorithm is a combination method using binary edge image gradient mapping and human potential distribution pattern of facial features . First apply the binary edge image of the horizontal direction , as well as mapping of the gradient of the vertical direction , to determine the position of the face , and the coarse position of the human eye , and then a position of the human eye using approximation method for precise positioning ; by the gradient of the image in the vertical direction mapping, to determine the position of the mouth ; combined with the position of the human eye , the position of the nozzle , as well as the potential distribution of the facial feature , and identify the position of the nose . At the same time , in order to verify the effectiveness of the algorithm , the algorithm was validated using a range of different people of different postures face . The combination of the two algorithms , people face feature extraction easier , reduces the computation has been presented an improved face annotation algorithm . Then summarized in facial expression feature extraction and expression recognition method , on the basis of analysis and generalization of a Gabor wavelet transform and K-nearest neighbor classification combined expression recognition method , and use this method BU 3 sub > DFE strength database identify facial expression analysis. First picture of the BU 3 DFE database contains expression information preprocessing , Gabor wavelet transform is then performed on the emoticon , to extract expression feature vector and expression be classified by the K-nearest neighbor algorithm . The final conclusion by comparing two intensity expression recognition results .
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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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