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Research of Face Recognition Technique under Variable Lighting and Pose

Author: LuChunMei
Tutor: NiuHaiJun
School: Xi'an University of Electronic Science and Technology
Course: Computer System Architecture
Keywords: Face Recognition Grayscale normalized The recent light than image Posture weight Weighted minimum distance classifier
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 117
Quote: 1
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Abstract


Since the 1990s , the rapid development of face recognition technology , face recognition under non- controlled conditions and with the conditions is a challenging subject , the face image illumination and pose changes lead to a sharp decline in the recognition rate , which both research has become difficult and hot face recognition field . Access to large amounts of data on the basis of a comparative analysis of the identification method , focusing on the study of the impact of changes in illumination and pose a new approach . Light intensity and illumination angle of illumination changes is divided into processing : gray-scale normalization preprocessing to reduce the sensitivity of the light intensity ; using the five basic point light source is approximated by the lighting conditions in the face recognition applications , the input image is estimated recently similar lighting conditions , pursuant to which the proposed \For attitude change , the image in the training set attitude judgment of training is divided into different attitude subset feature subspace construct different attitude , and put forward of weight \minimum distance classifier to assign different posture weight to calculate the integrated distance to complete the classification . On a theoretical basis , to achieve a face recognition system , comprehensive experiments to evaluate the application of commonly used indicators . The results show that this method , changes in light intensity, angle and attitude angle change more cases , significantly improve the recognition rate , and the training speed , high recognition speed , less number of samples of human face images less constrained .

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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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