An Efficient Ear Identification System

D. Kisku, Sandesh Gupta, Phalguni Gupta, J. Sing
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引用次数: 9

Abstract

This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. It makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are extracted from each color slice region as a part of invariant feature extraction. The extracted keypoints are then fused separately by the two fusion approaches, namely concatenation and the Dempster-Shafer theory. Finally, the fusion approaches generate two independent augmented feature vectors for identification of individuals separately. The proposed technique is tested on IIT Kanpur ear database of 400 individuals and is found to achieve 98.25% accuracy for identification of top 5 best matches.
一个高效的耳朵识别系统
本文提出了一种鲁棒耳识别系统,该系统融合了耳彩色切片区域的SIFT特征。利用高斯混合模型(GMM),利用矢量量化算法建立混合高斯耳模型,并在GMM框架中应用K-L散度,通过对比一对参考耳和探测耳的颜色相似度,记录指定范围内的颜色相似度。从每个颜色切片区域提取SIFT特征作为不变特征提取的一部分。然后,将提取的关键点分别通过拼接和Dempster-Shafer理论两种融合方法进行融合。最后,融合方法生成两个独立的增强特征向量,分别用于个体识别。在印度理工学院坎普尔400个个体的耳朵数据库中进行了测试,发现识别前5个最佳匹配的准确率达到98.25%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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