遮挡表情变化下的联合辅助字典学习与低秩分解人脸识别  

Occlusion expression variation face recognition based on auxiliary dictionary and low rank decomposition

作　　者：付晓峰[1] 张予 吴俊[1] 

机构地区：[1]杭州电子科技大学计算机学院,杭州310018

出　　处：《中国图象图形学报》2018年第3期399-409,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61672199;61100100);浙江省自然科学基金项目(Y1110232);杭州电子科技大学科研启动基金项目(KYS055609040)~~

摘　　要：目的从真实环境中采集到的人脸图片通常伴随遮挡、光照和表情变化等因素,对识别结果产生干扰。在许多特殊环境下,训练样本的采集数量也无法得到保证,容易产生训练样本远小于测试样本的不利条件。基于以上情况,如何排除复杂的环境变化和较少的训练样本等多重因素对识别效果的影响逐渐成为了人脸识别方向需要攻克的难题。方法以低秩矩阵分解为基础,分别使用非凸秩近似范数和核范数进行两次低秩矩阵分解,以达到去除遮挡干扰的目的。首先通过非凸稳健主成分分析分解得到去除了光照、遮挡等变化的低秩字典。为消除不同人脸类的五官等共通部分的影响,加快算法收敛效率,将得到的低秩字典用作初始化,进行基于核范数的第二次秩近似分解,以获得去除了类间不相关判别性的低秩字典用于分类。最后针对训练样本较少和遮挡样本占比过大等问题,选用同一数据库中不用做训练和测试的辅助数据作为辅助字典模拟可能出现的遮挡、光照等影响,通过最小化稀疏表示重构误差进行分类识别。结果选用AR库和CK+库分别进行实验。在AR库的实验中,通过调整训练图片中遮挡、光照和表情变化的样本比例来检测算法性能。其中,在遮挡图片占比分别为1/7和3/7的训练集中,无遮挡图片由无干扰和光照表情干扰图片联合组成。在遮挡图片占比为2/7的训练集中,无遮挡图片全由光照表情变化图片组成。实验结果表明,在多种实验情况下均获得较高识别率。其中根据不同遮挡比例,分别获得97.75%、92%、95.25%和97.75%、90%、95.25%等识别率。与同类算法对比提高3%5%。选用的外部数据从10类人脸至40类依次增加,获得的识别结果为96.75%98%,与同类算法相比提高了2%3%。在CK+表情库中,选用同伦算法配合分类求解,获得的识别结果为95.25%。结论本文提出了一种在克服复杂Objective Face recognition has an important role in our daily life. Applications are increasingly using face recognition as a useful interactive function. Real-life environments often introduce complex effects on data collection. Thus, face recognition always encounters challenges from several variations and occlusions. Face images collected from the real- world environment are usually accompanied with factors, such as occlusion, illumination, and expression changes, that in- terfere with recognition results. The number of collected training samples cannot be guaranteed in many special circumstances. Sometimes, the number of training samples is much smaller than the number of test samples. This paper aims to address difficult face recognition problems, such as eliminating complex environmental changes, undersampled training sets, and other recognition factors. Method This paper proposes an efficient face recognition algorithm under occlusion and the expressed variable, which is based on low rank approximation decomposition and auxiliary dictionary learning. It utilizes non-convex rank approximation norm and nuclear norm to perform matrix decomposition twice. The algorithm efficiently eliminates gross sparse errors on occlusion and other factors. First, we obtain the initial low-rank dictionary without illumination and occluded variation based on the non-convex robust principal component analysis algorithm to increase the convergence efficiency of the algorithm. Second, we perform the second low-rank decomposition based on a nuclear norm to obtain discriminant and incoherent low-rank dictionary and eliminate the influence of common facial parts through different classes. Finally, we choose the auxiliary data from the same database as an auxiliary dictionary that can simulate possible interfer- ence to overcome the problem of insufficient training samples and the large number of occlusion samples. The testing sam- ples can be classified by minimizing the reconstruction error. This study considered the variation of

关 键 词：人脸识别 低秩分解 字典学习 结构不相关 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]