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- 2018
一种基于加权图模型的手指静脉识别方法
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Abstract:
摘要： 提出一种基于加权图模型的手指静脉网络特征描述方法。对于一幅手指静脉图像,通过图像划分获得图的顶点集,利用三角剖分获得图的边集,边的权重由边所连接顶点之间的特征相似度决定。通过这种方式,一幅手指静脉图像可转化为一个加权图,并通过度量加权图邻接矩阵之间的相似度实现手指静脉识别。详细研究影响识别结果的几个因素,并通过试验证明了该方法的有效性。
Abstract: A new weighted graph construction method was proposed for finger-vein network representation. For a weighted graph, its nodes and edges were respectively generated by dividing image into blocks and a triangulation algorithm, and the weights of edges were valued using the feature similarities between adjacent blocks. In this way, a finger-vein image could be represented by a weighted graph, and the adjacency matrix of this weighted graph was used for finger-vein recognition. The experiment results proved the effectiveness of the method, and some important factors that affected graph recognition results were discussed in detail
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