基于手背、手掌和手指静脉融合的智能人体认证系统

Mona A. Ahmed, A. Salem
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引用次数: 2

摘要

多模态生物识别系统大致用于达到极高的识别精度。本文报道了一种新的多模态生物识别系统,该系统采用智能技术,通过融合手背、手掌和手指的静脉图案来进行人体身份验证。我们改进了一种图像分析技术,从手背、手掌和手指静脉图像中分离出感兴趣区域。在分离ROI后,我们构建了一系列预处理步骤,利用中值滤波、维纳滤波、对比度有限自适应直方图均衡化(CLAHE)和同态滤波对手背、手掌和手指静脉图像进行增强。我们的智能技术基于以下智能算法,即;主成分分析(PCA)算法进行特征提取,k-近邻(K-NN)分类器进行匹配操作。数据库选择博斯普鲁斯手静脉数据库、CASIA多光谱掌纹图像数据库V1.0 (CASIA数据库)和山东大学机器学习与应用同源多模态特征数据库(SDUMLA-HMT)。3种生物特征融合的最终结果为正确识别率(CRR)为99.21%,错误拒绝率(FRR)为0.04%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INTELLIGENT sYSTEM FOR HUMAN AUTHENTICATION USING FUSION OF DORSAL HAND, PALM AND FINGER VEINS
Multimodal biometric systems roughly used to achieve extreme recognition accuracy. This paper reports a novel multimodal biometric system employing intelligent technique to authenticate human by fusion of dorsal hand, palm and finger veins pattern. We improved an image analysis technique to separate region of interest (ROI) from dorsal hand, palm and finger veins image. After separating ROI we construct a sequence of preprocessing steps to enhance dorsal hand, palm and finger veins images using Median filter, Wiener filter, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Homomorphic filter to improve vein image. Our intelligent technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database selected was Bosphorus Hand Vein Database, CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) and the Shandong University Machine Learning and Applications Homologous Multi-modal Traits (SDUMLA-HMT). The accomplished result for the fusion of three biometric traits was Correct Recognition Rate (CRR) is 99.21% with False Reject Rate (FRR) 0.04%.
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