Learning LogDet Divergence for Ear Recognition
Learning LogDet Divergence for Ear Recognition
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DOI：
10.1145/3230820.3230832
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发表时间：
2018-05
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影响因子：
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通讯作者：
Ibrahim Omara;Ahmed M. Hagag;W. Zuo
中科院分区：
文献类型：
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作者：
Ibrahim Omara;Ahmed M. Hagag;W. Zuo
近年来，耳纹已成为最重要的生物特征识别技术之一，在不同领域有着广泛的应用，特别是在法医学领域。本文提出了一种基于融合局部描述子的人耳识别方法，用于特征提取，基于LogDot散度进行分类。具体使用二值化统计图像特征(BSIF)和边缘幅值方向模式(POEM)来表示人耳图像。然后，利用判别相关分析(DCA)算法对这些特征进行融合和降维。最后，采用基于LogDot散度的度量学习方法，通过学习近似最近邻(ANN)的马氏矩阵来识别人耳图像。实验结果是在四个可用的数据集上进行的；IIT德里I，II和USTB I，II数据集。该方法的性能优于现有方法，对IIT德里I、II和USTB I、II的识别率分别为98.4%、98.7%、100%和97.4%。
Ear-print has become one of the most important types of vital biometric in recent years; ear-print is using in different applications; especially in forensic science. In this paper, we present a novel approach for ear recognition based on fusion local descriptors for feature extraction, and LogDot divergence for classification. In details, binarized statistical image feature (BSIF) and patterns of oriented edge magnitude (POEM) are used to represent ear image. Then, discriminative correlation analysis (DCA) algorithm is exploited for fusion those features and reduction dimension. Finally, LogDot divergence based metric learning is adopted to recognize the ear images by learning a Mahalanobis matrix for approximate nearest neighbor (ANN) approach. The experimental results ar performed on four available datasets; IIT Delhi I, II and USTB I, II datasets. The proposed approach superior performance over the state-of-the-art approaches and can achieve promising recognition rates around 98.4%, 98.7%, 100% and 97.4% for IIT Delhi I, II, and USTB I, II, respectively.