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Research On Key Algorithms For Face Recognition Under Complex Illumination

Posted on:2007-02-28Degree:DoctorType:Dissertation
Country:ChinaCandidate:L S ZhuangFull Text:PDF
GTID:1118360185951426Subject:Circuits and Systems
Abstract/Summary:PDF Full Text Request
Face recognition is an active research topic in image processing and computer vision, which refers to pattern recognition, machine learning and so on. Face recognition has promising applications in public security, intelligent surveillance etc. Though face recognition has achieved great progress in the last 40 years, it is still a great challenge to build an automatic, high performance, high robust system for face recognition, due to the influence of illumination, pose and expression etc.One important purpose of this dissertation is to improve the recognition accuracy and robustness of face recognition system under various lighting conditions. To achieve this purpose, the focus of our work is on the image preprocessing, feature point location and feature extraction. The main work and innovation of this dissertation includes:1. The illumination normalization problem is studied, and an illumination preprocess algorithm based on total variation model is proposed.Illumination is one of the factors that reduce the recognition accuracy of face recognition system. In most cases, the difference between two images caused by illumination change is greater than that caused by individual difference. According to the irradiance lighting model, we propose a preprocess algorithm of illumination based on total variation model. Experimental results showed that our method could reduce the effect of illumination, and improve the recognition accuracy and robustness of face recognition system.2. The facial feature location problem is studied, and a robust facial feature point location algorithm under variable illumination is proposed.Facial feature location algorithms are important for an automatic face recognition system. It affects distinctly the recognition accuracy and robustness of the face recognition system. Active Shape Model (ASM) is a population method for face alignment, but its location accuracy is rough. Elastic bunch graph matching (EBGM) is another import location algorithm. It can achieve a fine accuracy when locating facial feature points, while its computational complexity is high and convergence is slow. By analyzing the merit and demerit of ASM and EBGM, we proposed a...
Keywords/Search Tags:Face Recognition, Illumination Processing, Facial Feature Points Location, Gabor Wavelet, Feature Extraction
PDF Full Text Request
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