摘要
为了解决判别稀疏邻域保持嵌入（DSNPE）算法中时间复杂度偏高的问题,构造了一种新类间离散度.用各类样本的平均向量组成过完备字典去重构表达每一类的平均向量,然后通过最大间距准则（MMC）构造新的目标函数,更好地展现人脸样本数据库类间的差异,增强了类间判别力和鲁棒性,简化了字典和字典表达,降低算法复杂度.实验结果表明：改进后的算法在保持识别率优势的前提下,极大地减少了识别时间.
The improved formulation of between-neighborhood scatter was proposed in order to try to solve the problem on time in the algorithm discriminant sparse locality and preserving projections（DSNPE）. It comes to select the mean vector set of each class as the over complete dictionary to represent the mean vector of each class. And then we formed a new target function through maximum margin criterion（MMC）. As a result,the improved algorithm reveals the between-class distance exactly,improves the robustness,simplifies the dictionary and representation,decreases complexity of the algorithm.The experiment manifests that the improved algorithm shortens the proceeding time heavily under the guarantee of recognition rate.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2018年第1期53-57,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
湖北省重大关键技术研发项目(2015ACA063)
中央高校基本科研业务费资助项目(2014TS041)
深圳市技术创新计划资助项目(CYZZ20140829104843693)
关键词
人脸识别
稀疏表达
局部保持
类内紧凑度
类间离散度
face recognition
sparse representation
local preserving
within-neighborhood scatter
between-neighborhood scatter
