【摘要】 为提高手背静脉识别过程中特征的有效性,提出了一种基于改进非负矩阵分解(NMF)的识别算法.首先,静脉图像经过分块后,将每一块子图像的像素均值与平均梯度幅值作为图像原始特征;其次,将所有训练样本原始特征形成的特征矩阵进行非负矩阵分解,其中对分解后的系数向量加以稀疏性与可区分性约束,从而形成改进的非负矩阵分解模型;再次,基于梯度投影法对提出的非负矩阵分解模型进行求解,获取新的特征基与特征向量;最后,利用最近邻匹配算法对特征向量进行分类,实现身份的识别.实验结果表明,提出的识别算法可获得较高的识别率,处理过程具有较好实时性.更多还原

【Abstract】 To enhance feature effectiveness during hand dorsal vein recognition,we propose a new recognition algorithm based on improved nonnegative matrix factorization( NMF). Firstly,after dividing vein image into blocks,we use the mean and the average gradient amplitude of sub-image as image original features. Secondly,we apply NMF in the feature matrix which is formed by combining the original feature vectors of all training samples,where the coefficient vectors are imposed by sparse and discriminant constraints,and the improved NMF model can be acquired. Thirdly,we use a projected gradient method to solve the NMF model,and new feature basis and feature vectors are obtained. Finally,new feature vectors are classified by K-nearest neighbour( KNN),and the vein object is identified successfully. Experiment results show that the proposed algorithm has high correct recognition rate and good real-time performance.更多还原