正则化双阶线性稀疏编码在人脸识别中的应用  

APPLYING DUAL-STAGE LINEAR REGULARISED SPARSE CODING IN FACE RECOGNITION

作　　者：李利正 黄煜栋 陈平生[1] 

机构地区：[1]绍兴职业技术学院计算机系,浙江绍兴312000

出　　处：《计算机应用与软件》2014年第11期222-224,285,共4页Computer Applications and Software

基　　金：浙江省教育技术研究规划课题(JB083)

摘　　要：传统的稀疏编码方法在遇到大规模数据时,因计算复杂度高而出现异常。针对这种异常导致不能很好地进行特征提取的问题,提出正则化双阶线性稀疏编码DLRSC(Double Linear Regularization Sparse Coding)方法。借助于广义多特征子空间框架来学习噪声和异常像素的结构特征,通过使用L1球理论,计算出唯一的近似解,并且利用滤波技巧避免了大规模数据的复杂计算,从而降低了时间及空间复杂度。最后,在ORL及Yale两大通用人脸数据库上的实验验证了所提的DLRSC方法的有效性,实验结果表明,相比其他几种最先进的稀疏编码方法,所提方法取得了更好的识别效果。Anomaly will occur in traditional sparse coding method when large-scale data is encountered due to high computational complexity,and this leads to the feature extraction not working well.In light of this problem,we propose the dual-stage linear regularised sparse coding（ DLRSC） method,it learns the structure features of noise and abnormal pixel by means of generalised multi-feature subspace framework,and calculates the only approximate solution by using the theory of L1 ball.Moreover,the filtering skill is adopted to avoid the complex computation of the large-scale data,thereby reduces the time and space complexity.Finally,the experiments on two universal database,the ORL and the Yale,verify the effectiveness of the mentioned DLRSC method,experimental results show that compared with other state-of-the-art sparse coding methods,the proposed method achieves better recognition results.

关 键 词：人脸识别 特征抽取 正则化 双阶线性 稀疏编码 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]