Hybrid SVM/HMM Method for Face Recognition

被引：1
|
作者：
刘江华
陈佳品
程君实
机构：
[1] Information Storage Research Center
[2] Shanghai Jiaotong University
来源：
Journal of Donghua University(English Edition) | 2004年 / 01期
基金：
中国国家自然科学基金;
关键词：
SVM; HMM; face recognition; probability output; wavelet transformation;
D O I：
10.19884/j.1672-5220.2004.01.007
中图分类号：
TP391.41 [];
学科分类号：
080203 ;
摘要：
A face recognition system based on Support Vector Machine (SVM) and Hidden Markov Model (HMM) has been proposed. The powerful discriminative ability of SVM is combined with the temporal modeling ability of HMM. The output of SVM is moderated to be probability output, which replaces the Mixture of Gauss (MOG) in HMM. Wavelet transformation is used to extract observation vector, which reduces the data dimension and improves the robustness. The hybrid system is compared with pure HMM face recognition method based on ORL face database and Yale face database. Experiments results show that the hybrid method has better performance.
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页码：34 / 38
页数：5
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