摘要
针对目前手背静脉图像识别采用细化和骨架操作等提取结构特征易造成静脉结构细节丢失和特征点误判等问题,提出一种基于方向梯度直方图(HOG)的手背静脉特征识别方法。采用生物特征识别的一般流程,对手背静脉图像灰度进行归一化和滤波增强等预处理后,直接对手背静脉灰度图像进行二级小波包分解,提取低频子带图的HOG纹理特征,最后采用K近邻分类器实现个人身份识别。利用自行建立的手背静脉图像数据库对所提方法进行验证,结果证明了算法的有效性,其正确识别率为95%,应用前景广阔。
For the current hand vein image recognition using the extraction structure features such as refinement and skeleton operations,it＇s easy to cause the loss of vein structure details and misjudgment of feature points,this paper proposed a hand vein feature recognition algorithm based on gradient histogram gradient（HOG）.Adopting general biometric identification process,this algorithm extracts the HOG texture feature of the low-frequency sub-band graph by the directly decomposing two-level wavelet packet after the hand dorsal vein image is preprocessed by image grey normalization pretreatment and filtering enhancement.Then,the personal identity is recognized by using Kneighbor classifier.This algorithm was verified finally by using self-established dorsal vein image database.The experimental results show that the proposed algorithm is effective and its correct recognition rate is 95%,and its application prospect is broad.
作者
严娇娇
种兰祥
李婷
YAN Jiao-jiao, CHONG Lan -xiang, L1 Ting(1College of Information Science and Technology, Northwest University, Xi＇ an 710127, Chin)
出处
《计算机科学》
CSCD
北大核心
2018年第B06期206-209,共4页
Computer Science
