计算机工程2012，Vol.38Issue(12)：125-128,4.DOI:10.3969/j.issn.1000-3428.2012.12.037
贝叶斯模型下基于SIFT特征的人脸识别
Face Recognition Based on SIFT Feature in Bayesian Model
摘要
Abstract
To handle the influences brought by the change of pose and expression. Scale Invariant Feature Transform(SIFT) descriptors, which is rotating and scale invariant, is applied to measure the similarity between corresponding sub-regions of two faces, and the probabilistic similarity models of the same or different faces under various deformations are built with Gaussian Mixture Model(GMM). Then, a probabilistic frame which is based on Bayesian formula is established to get the recognition results, combining with the weight of each sub-region which is decided by their peculiarities. Experimental results indicate that the proposed method outperforms the traditional SIFT-based method when the variation of the pose or expression is large.关键词
人脸识别/尺度不变特征变换描述子/贝叶斯概率模型/姿态/表情/子区域Key words
face recognition/ Scale Invariant Feature Transform(SIFT) descriptor/ Bayesian probabilistic model/ pose/ expression/ sub-region分类
信息技术与安全科学引用本文复制引用
张龙媛,陈莹..贝叶斯模型下基于SIFT特征的人脸识别[J].计算机工程,2012,38(12):125-128,4.基金项目
国家自然科学基金资助项目(61104213) （61104213）
江苏省自然科学基金资助项目(BK2011146) （BK2011146）
上海变通大学系统控制与信息处理教育部重点实验室开放课题基金资助项目(SCIP2011008) （SCIP2011008）
