融合SIFT特征和保局投影的人脸识别  

Face Recognition Based on SIFT Feature and Locality Preserving Projection

作　　者：周昕[1] 翁建广[2] 

机构地区：[1]杭州职业技术学院信息电子系,杭州310018 [2]浙江传媒学院新媒体系,杭州310018

出　　处：《科技通报》2013年第9期142-148,共7页Bulletin of Science and Technology

基　　金：浙江省自然科学基金(Y1100189;LY12F02008)

摘　　要：提出一种人脸识别方法用于解决姿态变化对识别准确率的影响。首先检测人脸图像的SIFT特征,然后根据SIFT特征计算人脸图像间的多示例距离;基于此多示例距离,用保局投影将人脸图像映射至流形空间,最后在流形空间中采用K近邻方法进行人脸识别。该方法有三个特点:(1)采用SIFT特征减小了未知姿态对识别准确率的影响;(2)通过保局投影将特征变换到流形空间一个点,避免了复杂的SIFT特征匹配策略;(3)借助流形方法滤除高维特征中的噪声。实验结果表明与已有方法相比,在人脸姿态不确定的情况下,该方法能提供较为理想的识别准确率。This paper present an approach to pose-invariant face recognition. Firstly, SIFT features are detected in each face image and the distance between two faces is determined in a multi-instance manner according to the SIFT features. Based on the distance, we project features into manifold space via Locality Preserving Projection. The recognition is finally done in manifold space through KNN algorithm. Our approach has three advantages： （1） the influence of unknown pose is diminished by using of SIFT features; （2） SIFT features are projected into manifold, which avoid complex strategy for SIFT feature matching; （3） Manifold is helpful for removing unknown noise from high-dimensional features. Experiments show that our approach provides exciting recognition rate under unknown pose compared with existed methods.

关 键 词：SIFT特征 多示例距离 保局投影 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]