摘要
提出了一种新的虹膜特征提取与识别方法。对虹膜纹理采用最大判别熵的独立分量分析(ICA-MJE)实现特征提取,通过支持向量机(SVM)完成模式匹配。与Gabor小波的方法比较,在编码长度和编码时间方面有明显地改进。实验结果表明,该算法能更好地提高虹膜的识别率并能够有效地应用于身份识别系统中。
A new method for iris feature extraction and recognition was proposed in this paper. Feature was extracted with independent component analysis by maximizing J-divergence entropy （ICA-MJE）, and then Support Vector Machine （SVM） was used to match two iris codes. Compared with that of Gabor wavelet method, the size of an iris code and the processing time of the feature extraction were significantly reduced. Experimental results show that the developed system with high iris recognition rate could be used for a personal identification system in a more efficient and effective manner.
出处
《计算机应用》
CSCD
北大核心
2007年第6期1505-1507,共3页
journal of Computer Applications
关键词
虹膜识别
特征提取
独立分量分析
支持向量机
判别熵
iris recognition
feature extraction
independent component analysis
Support Vector Machine
J-divergence entropy
