数据采集与处理2016，Vol.31Issue(5)：1043-1050,8.DOI:10.16337/j.1004-9037.2016.05.023
基于压缩金字塔核稀疏表示的人脸识别
Face Recognition Based on Compressed Spatial Pyramid Model and Kernel Sparse Repre-sentation
摘要
Abstract
Face recognition is still challenging due to the large variations of facial appearance,caused by lighting,partial occlusions,head pose,etc.The feature extraction is a key step for face recognition.In order to improve the recognition rate of face recognition,we introduce a novel feature extraction technique for face recognition,which is a combination of compressed sensing and spatial pyramid model method. The scale invariant feature transform is first used to be a feature extractor to obtain facial features.Then by using sparse coding in the randomly generated dictionary,dimensionalities of those features are re-duced.After the spatial pyramid is used to be a feature extractor to obtain different spatial scales,the max pool is used to integrate the features.Finally,the kernel sparse representation classifier is proposed to classify the features to complete the face recognition.The experimental results based on the Extended Yale B,AR and CMU PIE databases demonstrate that the method has a strong robustness in the illumi-nation,pose and disguise variation with a faster running speed.关键词
人脸识别/空间金字塔/压缩感知/稀疏表示Key words
face recognition/spatial Pyramid/compressive sensing/sparse representation分类
信息技术与安全科学引用本文复制引用
周凯,元昌安,覃晓,郑彦,苏杰波..基于压缩金字塔核稀疏表示的人脸识别[J].数据采集与处理,2016,31(5):1043-1050,8.基金项目
国家自然科学基金(61363037)资助项目。 （61363037）
