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Investigation and Application of the Algorithm of Face Detection and Face Recognition

Author: LiuLe
Tutor: LiuZhiJing
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Face Detection Face Recognition Skin color model Discrete Cosine Transform Hidden Markov Models
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 253
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Abstract


Face detection and recognition technology to pattern recognition , most theoretical value and application prospects in the field of computer vision , and one of the challenging research topic . Its purpose is to make computers , like people, have the ability to find whether there face from an image , as well as the discovery of the human face authentication . In this paper, the current mainstream face detection and recognition technology comparative analysis on the basis of in-depth study of how to improve the accuracy and speed of face detection and recognition , while ensuring that the system is robust , and ultimately achieve a face detection and recognition system . Through evaluation experiments on the system , as well as statistical analysis of experimental data to prove the basic face detection and recognition methods used in this paper to achieve the above objectives . This paper studies the work is as follows : In the Face Detection : take the combined method of skin detection and human facial feature spatial orientation . First , according to the statistical distribution of skin color in the color space and clustering features to select the most suitable color space and color face detection model . Then skin color segmentation, to obtain the area of ??the skin may be in the face image . Again using the statistical characteristics of the face region and traditional priori knowledge, screened from the determined areas of the skin area may belong to a human face . Finally , based on the facial feature extraction and verification of spatial location , precise positioning of the candidates face . The experimental results show that the method is effective and feasible . Face Recognition : a discrete cosine transform on the original image , the low frequency part of the discrete cosine transform coefficients is selected as the hidden Markov model of the observation vectors , for training . Dimensional hidden Markov model for the two-dimensional face image modeling to better reflect the face of individual differences . The experimental results show that a high recognition accuracy can be obtained using this method . Completed a large number of test experiments of face detection and recognition system . Show that the system in the face detection and recognition of lighting conditions , changes in the expression of the change of face pose strong robustness , detection and recognition rate of more than 95% under a variety of complex conditions , while achieving the high recognition speed .

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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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