作　　者： (崔益峰）; (李开宇）; (胡燕）; (徐责力）; (王平）;

机构地区： 南京航空航天大学自动化学院,南京211106

出　　处： 《中国图象图形学报》 2017年第9期1222-1229,共8页

摘　　要： 目的由于受到光照变化、表情变化以及遮挡的影响,使得采集的不同人的人脸图像具有相似性,从而给人脸识别带来巨大的挑战,如果每一类人有足够多的训练样本,利用基于稀疏表示的分类算法(SRC)就能够取得很好地识别效果。然而,实际应用中往往无法得到尺寸大以及足够多的人脸图像作为训练样本。为了解决上述问题,根据基于稀疏表示理论,提出了一种基于联合判别性低秩类字典以及稀疏误差字典的人脸识别算法。每一类的低秩字典捕捉这类的判别性特征,稀疏误差字典反映了类变化,比如光照、表情变化。方法首先利用低秩分解理论得到初始化的低秩字典以及稀疏字典,然后结合低秩分解和结构不相干的理论,训练出判别性低秩类字典和稀疏误差字典,并把它们联合起来作为测试时所用的字典;本文的方法去除了训练样本的噪声,并在此基础上增加了低秩字典之间的不相关性,能够提高的低秩字典的判别性。再运用l1范数法(同伦法)求得稀疏系数,并根据重构误差进行分类。结果针对Extended Yale B库和AR库进行了实验。为了减少算法执行时间,对于训练样本利用随机矩阵进行降维。本文算法在Extended Yale B库的504维每类32样本训练的识别结果为96.9%。在无遮挡的540维每类4样本训练的AR库的实验结果为83.3%,1 760维的结果为87.6%。有遮挡的540维每类8样本训练的AR库的结果为94.1%,1 760维的结果为94.8%。实验结果表明,本文算法的结果比SRC、DKSVD(Discriminative K-SVD)、LRSI(Low rank matrix decomposition with structural incoherence)、LRSE+SC(Low rank and sparse error matrix+sparse coding)这4种算法中识别率最高的算法还要好,特别在训练样本比较少的情况下。结论本文所提出的人脸识别算法具有一定的鲁棒性和有效性,尤其在训练样本较少以及干扰较大的情况下,能够取得很好地识别效果,适合在实 Objective Face recognition encounters significant challenges, particularly when images from different persons are similar to one another due to variations in illumination, expression, and occlusion. If we have sufficient training ima-ges of each person, which can span the facial variations of that person under testing conditions, then sparse representation- based classification （SRC） can achieve promising results. In many applications, however, the problems of small size of samples and lack of sufficient training images for each person are particularly significant. To solve these problems, this study presents a joint face recognition algorithm between a low-rank class dictionary and sparse error dictionary learning based on theory of sparse representation. The low-rank dictionary of each individual is a class-specific dictionary that cap- tures the discriminative feature of an individual. The sparse error dictionary represents intra-class variations, such as illu- mination and expression changes. Method An initial low-rank decomposition dictionary and a sparse dictionary are ob- tained based on theory of low-rank decomposition. Then, combining theories of low-rank decomposition and structural ir- relevance, the discriminative low-rank class dictionary and the sparse error dictionary are trained and subsequently merged as the dictionary that will be applied to the test part. Our method decomposes raw training data into a set of representative bases with corresponding sparse errors to efficiently model face images. We further promote structural incoherence among the bases learned from different classes. These bases are encouraged to be as independent as possible due to the regulariza- tion on structural incoherence. Additional discriminating capability is provided to the original low-rank models to improve performance. A sparse coefficient is acquired according to the L1 norm method （homotopy method）. Test samples can be classified based on the reconstruction error. Result Experiments are conducted on the E

关 键 词： 低秩类字典 稀疏误差字典 结构不相干 人脸识别