摘要
提出了一种基于局部信息统计的人耳识别方法。该方法将一幅人耳图像分成若干个子区域,分别提取每个子区域的分类特征,将各个子区域的特征串联为一个特征向量构筑人耳特征矢量,更加全面描述了人耳图像的局部与结构信息,应用最近邻分类器进行模式分类。采用三种不同的特征提取方法,以USTB人耳图像库对算法进行测试,实验结果表明,与全局信息比较同种方法识别率提高30%以上,验证了局部信息方法的有效性。
A new human ear recognition approach, based on statistic features of local information, is proposed. A human ear gray-scale image is divided into several sub-regions. The assorted features of each sub-regions are abstracted. Feature of sub-regions are joined in- to a feature vector to build human ear feature vector, thus gives a comprehensive description about the structure and local information of human ear images. The pattern classification is implemented by applying the nearest neighbor classifier. Three different feature ex- traction methods are adopted and USTB human ear database are applied to testing this algorithm, The experimental results show that the recognition rate is improved more than 30% compared with global information and the method based on statistics feature of local in- formation is proved effective.
出处
《计算机工程与应用》
CSCD
2012年第9期141-144,共4页
Computer Engineering and Applications
基金
辽宁省博士启动基金(No.20111015)
关键词
人耳识别
局部信息
统计特征
傅里叶变换
ear recognition
local information
statistic features
Fourier transform
