基于Gabor小波和支持向量机的掌纹识别算法的研究

THE RESEARCH ON PALMPRINT REGCOGNITION USING GABOR WAVELET AND SUPPORT VECTOR MACHINE

掌纹识别作为1种新兴的生物识别技术,因其识别区域大、易采集、精度高和可靠性高等优点得到了较快的发展。本文提出基于Gabor小波和支持向量机的掌纹识别算法。算法主要分三个步骤,首先将掌纹图像用5个尺度4个方向的2DGabor滤波器组对图像进行滤波并下采样得到Gabor特征矩阵,之后用二维主成分分析(two-dimen-sional principle component analysis2,DPCA)进行降维,最后将得到的特征向量送进支持向量机(support vector machine,SVM)进行学习分类。实验结果表明,该算法能够很好的解决小样本识别问题,有效的提高掌纹识别率。

As an emerging biometric technology,palmprint recognition technology has been developed quickly because of the advantages of its large recognition region,and easy collection,high precision and reliability.This paper proposes a palmprint identification algorithm based on Gabor wavelet and support vector machine（SVM）.Three steps are involved in the algorithm.First,the palmprint image was filtered by a bank of 2DGabor wavelets with five scales and four directions and downsampled to form Gabor feature matrix.Then two-dimensional principle component analysis（2DPCA） was used to extract the features into a lower dimension space.Last,SVM was used to classify the feature vectors.Experimental results showed that this algorithm could solve the small sample recognition problem and improve palmprint recognition rate significantly.