基于视觉词袋模型的人脸识别方法  

Face recognition based on bag-of-visual word model

作　　者：崔建涛[1] 范乃梅[1] 邓璐娟[1] 

机构地区：[1]郑州轻工业学院软件学院,郑州450002

出　　处：《华中师范大学学报（自然科学版）》2015年第1期25-28,51,共5页Journal of Central China Normal University：Natural Sciences

基　　金：国家自然科学基金项目（61040025）

摘　　要：近年来,基于视频的人脸识别吸引了很多人的关注,同时,视觉词袋模型已成功应用于图像检索和对象识别中.论文提出了一种基于视觉词袋模型的人脸识别方法,该方法首先在兴趣点提取尺度不变特征变换的图像描述,这些兴趣点由高斯差分检测,然后基于k均值生成视觉词汇,并使用视觉单词的索引以取代这些描述符.然而,在人脸图像中,由于面部姿势失真,面部表情和光照条件变化,采用尺度不变特征变换描述符后识别效果并不理想.因此,论文使用仿射尺度不变特征变换描述符作为人脸图像表示法.在Yale及ORL人脸数据库上的实验结果表明,在人脸识别中,本文方法可以获得较低的错误率.Recent years,face recognition based on video has been concerned by more and more persons.At the same time,bag-of-visual words（BoWs）representation has been successfully applied in image retrieval and object recognition recently.In this paper,a video-based face recognition approach which uses visual words is proposed.In classic visual words,scale invariant feature transform（SIFT）descriptors of an image are firstly extracted on interest points detected by difference of Gaussian（DoG）,then k-meansbased visual vocabulary generation is applied to replace these descriptors with the indexes of the closet visual words.However,in facial images,SIFT descriptors are not good enough due to facial pose distortion,facial expression and lighting condition variation.In this paper,we use Affine-SIFT（ASIFT）descriptors as facial image representation.Experimental results on Yale and ORL Database suggest that proposed method can achieve lower error rates in face recognition task.

关 键 词：人脸识别 视觉词袋 图像检索 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]