基于小波多分辨率分析的指横纹定位新算法  

A New Location Algorithm of Knuckleprint Based on Wavelet Multi-Resolution Analysis

作　　者：毛贤光[1] 赖晓铮[1] 赖声礼[1] 戴宏跃[1] 

机构地区：[1]华南理工大学电子与信息学院,广州510640

出　　处：《计算机研究与发展》2009年第4期629-636,共8页Journal of Computer Research and Development

摘　　要：针对指横纹感兴趣区域(ROI)难以准确定位的问题,在明确提出指横纹ROI定义的前提下,提出利用小波多分辨率分析进行指横纹ROI自动检测定位的新算法.该方法利用纹理相似性原理,在高频子图采用基于特征向量和区域生长法产生指横纹的候选子区域集合,然后利用低频子图Radon投影得到的指横纹的区域特征对候选子区域进行验证,最后结合直线拟合手指轮廓得到指横纹在原图的有效位置,最终实现指横纹ROI的精准定位.实验证明该算法不仅能有效克服噪声以及手形姿态变化的影响,并且对多种情况有很强的鲁棒性.Being a new biometrical characteristic, knuckleprint has drawn considerable attention in personal identification. But owing to its defects such as weak resistance to noise, easy affection by hand-shape, etc. , the location of region of interest （ROD of knuckleprint is more difficult than other biometrical characteristics of palm. In order to solve the problem of accurate location of ROI of knuckleprint, by defining ROI of knuckleprint as the minimal region containing the total information of knuekleprint, a new automatic detection and location algorithm is brought forward based on wavelet multi-resolution and Radon projection to locate the ROI of knuckleprint accurately. Based on the texture similar theory, this algorithm divides knuckleprint image into multi-dimension images which include several high frequency sub-images and low frequency sub-images by wavelet multi-resolution, and then uses feature vector and regional growth to produce candidate sub-region set in high frequency sub-images. After that, the algorithm utilizes Radon projection in low frequency sub-images to verify the candidate sub-region set obtained from high frequency sub-images. Finally, by adopting straightline fitting technique, the location of ROI of knuckleprint in original image is accurately located. Emulation experiment shows that this algorithm not only can get rid of noise and hand-shape affection but also can keep robust in different situations.

关 键 词：指横纹 小波分析 多分辨率分析 Radon投影 直线拟合 ROI定位 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]