摘要
针对人脸识别中的特征提取问题,本文提出了一种结合Gabor小波特征和判别保局投影的人脸识别算法—GDLPP。该算法首先对人脸图像进行多分辨率的Gabor小波变换,提取样本的高阶统计信息;然后更改保局投影(LPP)的目标函数,增加样本类间散布约束,从而提取更具判别性的特征。本文采用最小近邻分类器估算识别率。在USPS数据库、Yale人脸库以及AR人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,GDLPP都具有较好的识别率。
In view of the problems of feature extraction in face recognition, a new face image feature extraction and recognition method-Gabor Discriminant Locality Preserving Projections （GDLPP） is proposed in this paper. GDLPP first gets the high-order statistical information by calculating the Gabor wavelet representation of face images. Based on LPP, GDLPP takes into account the inter-class information, changes the objective function, and extracts the discriminant feature of face for recognition. The proposed method was tested and evaluated in the USPS database, Yale face database and AR face database. Nearest neighborhood algorithm was used to construct classifiers. The experimental results show that GDLPP has good performance even if pose, illumination, face expression and train sample number change.
出处
《光电工程》
CAS
CSCD
北大核心
2008年第6期108-112,共5页
Opto-Electronic Engineering
基金
国家杰出青年科学基金项目(60788101)
浙江省重大科技攻关国际合作项目(2006C14026)
国家自然科学基金项目(60775016)
