一种2D+3D人耳融合识别方法研究.pdf

第26卷摇 第9期摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 模式识别与人工智能摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 Vol.26摇 No.9 摇 2013年9月摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 PR AI摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 Sep.摇 2013 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 摇 一种2D+3D人耳融合识别方法研究* 袁摇 立摇 摇 穆志纯 (北京科技大学 自动化学院摇 北京 100083) 摘摇 要摇 提出一种2D和3D模式相融合的人耳识别方法.利用基于Adaboost算法的人耳检测器在2D 图像上进行 人耳检测,在对应的深度图像中定位出人耳区域.对于2D人耳图像,利用核Fisher鉴别分析法进行特征提取,再利 用最近邻分类器进行识别;对于3D人耳深度图,利用3D局部二值模式进行特征提取,结合几何约束和位置约束在 测试耳和注册原型耳上进行特征点匹配,并利用匹配点数进行识别.最后将两者进行决策层融合.在UND人耳图 像库上的实验结果表明,与单独2D或3D人耳识别相比,文中提出的2D+3D人耳融合识别方法在光照变化情况下 能取得更好的识别性能. 关键词摇 人耳识别,3D局部二值模式,决策层融合 中图法分类号摇 TP391 A2D+3D Multimodal Ear Recognition Method YUAN Li,MU Zhi鄄Chun (School of Automation and Electrical Engineering,University of Science and Technology Beijing, Beijing 100083) ABSTRACT A2D+3D multimodal ear recognition method is proposed. Firstly,ear detection method based on Adaboost algorithm isusedtodetectearpartonthe2Dimages,thenthecorrespondingearpartislocated and extracted in the 3D range image. For 2D ear recognition,Kernel Fisher Discriminant Analysis is applied for feature extraction and Nearest Neighbor classifier is applied for ear recognition. For 3D ear recognition,3D Local Binary Pattern descriptor is applied for feature extraction on range image, geometric constraint and location constraint are used to perform the matching process between a test ear and aregisteredprotocolear,andearrecognitionperformanceisevaluatedbythenumberofthematching points. Finally,Bayes decision rule is used for the decision level fusion of 2D and 3D ear recognition classifiers. The experimental results on th