摘要
提出了一种新的多级分类的人脸识别算法。在类别数较大的人脸识别系统中,要想用一种特征一次性地把所有类别都分开是不现实的。首先使用快速的二维投影在大范围内找出测试样本的若干候选类别,然后对不同的测试样本分别在其候选子集中抽取特征,进而判断测试样本属于哪个类别。在200人的人脸库上进行了实验,识别率由71.23%提高到83.75%。
This paper presented a novel multilevel classification method. In practical face recognition system, the number of the classes is usually large. It caused the correct recognition rate not satisfied if only using one features classified the all probe samples one times. First, marked 20 candidate classes for each probe sample by a two dimensional projection approach. Se- cond, for each probe sample, employed 20 class＇ s candidate training samples to calculate the optimal discriminant vectors, then projected the probe sample and the candidate samples into the vectors and classified the probe sample. The proposed algorithm was evaluated by a 200 persons FERET face database. For elimination the illumination change, adjusted every image＇ s mean and standard deviation to 0.5 and 0.15 respectively. The 10 times experiments average accurate rate directly using combined discriminant analysis is 71.23%, while the average rate of multilevel classification is 83.75%.
出处
《计算机应用研究》
CSCD
北大核心
2008年第10期3069-3071,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(60503026)
关键词
人脸识别
特征抽取
多级分类
组合鉴别
face recognition
feature extraction
multilevel classification
combined discriminant analysis
