摘要
近年来,基于表示法的人脸识别技术主要都集中在约束条件和字典学习。很少有研究用样本数据特征来确定基于表示分类算法的性能。本文定义了结构离散度,表示样本集的结构特征。实验结果表明,具有较高的结构离散度的集合能让一个分类算法获得更高的识别率。
In recent years,representation-based face-recognition techniques are focus mainly on constraint conditions and dictionary learning. Few researchers study which sample data features determine the performance of representation-based classification algorithms.we define the structure-scatter degree, which represents the structure features of training sample sets, said structure characteristics of sample set. Experimental results show that sets with a higher structure scatter more likely allows a classification algorithm to obtain a higher recognition rate.
出处
《电子测试》
2016年第3X期61-62,共2页
Electronic Test
关键词
模式识别
人脸识别
Pattern recognition
Face recognition
