Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (24): 17-19.

• 博士论坛 • Previous Articles     Next Articles

Application of wavelet transform and block statistic to palmprint recognition

LIU Yuqin1,2,YUAN Weiqi1,GUO Jinyu2   

  1. 1.Computer Vision Group,Shenyang University of Technology,Shenyang 110870,China
    2.School of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-08-21 Published:2011-08-21

小波变换与分块统计在掌纹识别中的应用

刘玉芹1,2,苑玮琦1,郭金玉2   

  1. 1.沈阳工业大学 视觉检测技术研究所,沈阳 110870
    2.沈阳化工大学 信息工程学院,沈阳 110142

Abstract: Palmprint recognition for identification provides a new scheme for information security.This paper presents a combination method of transform domain and statistic domain for palmprint identification.The method filters Region Of Interest(ROI) of the palmprint with median filtering and decomposes it into several sub-images with the wavelet transform.Then it blocks the high-frequency sub-image.The mean and the variance of high-frequency coefficients for each sub-block are found.Their combination constitutes feature vector for the image.The nearest neighbor classifier is used to classify the images.The method is tested on the basis of UST palmprint image database.From the experimental result of 95.5% recognition rate,the method is better than the sub-space methods that are used for palmprint identification at present.

Key words: biometrics recognition, palmprint recognition, wavelet transform, block statistic, high-frequency coefficients

摘要: 用于身份鉴别的掌纹识别为信息安全提供了一种新的方案。提出一种变换域和统计域相结合的掌纹识别方法。对掌纹感兴趣区域(ROI)进行中值滤波再多级小波分解,对所有的高频子图像进行分块,求取每一子块高频系数的均值和方差,它们的组合构成该图像的特征向量,利用简单的最近邻分类器进行分类。运用UST掌纹图像库,对该算法进行了测试。从识别率为95.5%的实验结果看,该方法优于目前在掌纹识别上使用较多的子空间法。

关键词: 生物特征识别, 掌纹识别, 小波变换, 分块统计, 高频系数