模式识别与人工智能
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模式识别与人工智能  2006, Vol. 19 Issue (5): 585-590    DOI:
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基于分块统计量的Gabor特征描述方法及人脸识别*
龙飞1,2,叶学义1,李斌1,姚鹏1,庄镇泉1
1.中国科学技术大学 电子科学与技术系 合肥 230026
2.厦门大学 软件学院 厦门 361005
Block Statistics Based Gabor Feature Representation and Its Application to Face Recognition
LONG Fei1,2, YE XueYi1, LI Bin1, YAO Peng1, ZHUANG ZhenQuan1
1.Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026
2.Software School, Xiamen University, Xiamen 361005

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摘要 Gabor小波是人脸特征描述中的一个重要工具.为减少由直接对Gabor特征进行下采样造成的有用信息丢失,本文提出一种基于分块统计量的Gabor特征描述方法,增强人脸图像的Gabor特征描述效率.在此基础上,探讨基于广义鉴别分析的二次特征提取方法.实验表明,Gabor特征描述和广义鉴别分析两种方法结合后所产生的识别性能优于其中每个方法单独使用的识别性能,且与Eigenfaces、Fisherfaces等流行方法相比具有较大优势.
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龙飞
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庄镇泉
关键词 人脸识别Gabor小波分块统计量广义鉴别分析    
Abstract:Face representation based on Gabor features has attracted much attention and achieved great success in face recognition for some favorable attributes of Gabor wavelets such as spatial locality and orientation selectivity. A large number of Gabor features are produced with varying parameters in the position, scale and orientation of filters. In some existing methods, useful discriminatory information may be lost due to downsampling Gabor features directly. To reduce the loss, a block statistics based Gabor feature representation method is proposed. The effectiveness of this method is demonstrated by template matching test on ORL face database, and the comparative test results show that this method can yield better recognition accuracy with much fewer Gabor features as well as less CPU time of feature matching than the existing approach of downsampling based Gabor feature representation. In addition, Generalized Discriminant Analysis (GDA) which performs dimensionality reduction to Gabor features is used to produce more compact and discriminatory face representation. The experimental results of face recognition using different similarity measures show that the proposed method outperforms the famous Eigenfaces and Fisherfaces methods significantly, and the rationality of this combination is also comparatively demonstrated.
Key wordsFace Recognition    Gabor Wavelets    Block Statistics    Generalized Discriminant Analysis   
收稿日期: 2005-06-03     
ZTFLH: TP391.41  
基金资助:国家自然科学基金重大资助项目(No.90104030)
作者简介: 龙飞,男,1977年生,讲师,博士,主要研究方向为人脸识别、机器学习、图像处理.E-mail: flong@xmu.edu.cn.叶学义,男,1973年生,博士研究生,主要研究方向为计算机视觉、模式识别、信息安全.李斌,男,1970年生,副教授,主要研究方向为智能信息处理、机器学习.姚鹏,男,1974年生,博士研究生,主要研究方向为智能信息处理,生物特征识别.庄镇泉,男,1938年生,教授,博士生导师,主要研究方向为智能信息处理、生物特征识别、多媒体技术.
引用本文:   
龙飞,叶学义,李斌,姚鹏,庄镇泉. 基于分块统计量的Gabor特征描述方法及人脸识别*[J]. 模式识别与人工智能, 2006, 19(5): 585-590. LONG Fei, YE XueYi, LI Bin, YAO Peng, ZHUANG ZhenQuan. Block Statistics Based Gabor Feature Representation and Its Application to Face Recognition. , 2006, 19(5): 585-590.
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