智能系统学报2012，Vol.7Issue(3)：271-277,7.DOI:10.3969/j.issn.1673-4785.201112003
使用稀疏约束非负矩阵分解算法的跨年龄人脸识别
An age-span face recognition method based on an NMF algorithm with sparseness constraints
摘要
Abstract
For face recognition technology, apart from lighting, gesture, and expression factors, variations in shape and texture of human faces due to aging factors also significantly affect the performance of face recognition systems. Using a sparse-constrained non-negative matrix factorization (NMF) algorithm, a facial aging simulation method based on an improved prototype was first proposed and then applied to age-span face recognition to add virtual samples and heighten the recognition rate. Experimental results show that the age span indeed has a great effect on face recognition; the NMF algorithm has stronger feature extraction ability when the coefficient matrix is sparsely constrained. Furthermore, the recognition ratio is apparently improved after adding additional virtual samples by aging simulation of face texture features.关键词
人脸识别/跨年龄人脸识别/非负矩阵分解算法/稀疏约束/人脸老化模拟/虚拟样本Key words
face recognition/ age-span face recognition/ non-negative matrix factorization algorithm/ sparseness constraints/ facial aging simulation/ virtual samples分类
信息技术与安全科学引用本文复制引用
杜吉祥,翟传敏,叶永青..使用稀疏约束非负矩阵分解算法的跨年龄人脸识别[J].智能系统学报,2012,7(3):271-277,7.基金项目
国家自然科学基金资助项目(61175121) （61175121）
教育部新世纪优秀人才支持计划资助项目(NCET-10-0117) （NCET-10-0117）
福建省自然科学基金资助项目(2011J01349) （2011J01349）
福建省高等学校杰出青年科研人才培育计划资助项目 (JA10006) （JA10006）
福建省教育厅科技计划资助项目 (JA11004) （JA11004）
华侨大学侨办科研基金资助项目(11QZR05) （11QZR05）
华侨大学基本科研业务费专项基金资助项目 (JB-SJ1003). （JB-SJ1003）
