Ear recognition based on kernel principal component analysis and support vector machine
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摘要: 對人耳識別中若干關鍵問題進行了研究.介紹了兩種人耳圖像歸一化處理的方法,即基于外耳輪廓長軸的線標記法和基于外耳輪廓起始點的點標記法,并對這兩種方法進行了對比.在分析現有人耳識別方法不足的基礎上,提出利用核主元分析法提取人耳圖像的代數特征,再利用支持向量機分類模型進行人耳識別.在帶有角度、光照變化的北京科技大學人耳圖像庫上得到的識別率為98.7%,表明了該識別方法的有效性以及利用人耳圖像進行身份識別的可行性.Abstract: Some key issues in ear recognition were investigated. Two ear extraction and normalization methods, the mark line (long axis of the outer ear contour) based method and the mark points (the start and end points of the outer ear contour) based method, were proposed for recognizing ear images in the USTB ear database. Based on the analysis of the recent advances in ear recognition methods, the kernel principal component analysis (KPCA) was applied for ear feature extraction, and the support vector machine (SVM) model was applied for ear recognition. The ear recognition rate on USTB ear database with pose variation and lighting variation was 98.7%. The experimental result indicates the effectiveness of this method and proves the feasibility of ear recognition to be used in the field of personal authentication.
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