一种2D＋3D人耳融合识别方法研究  

A 2D+3D Multimodal Ear Recognition Method

作　　者：袁立[1] 穆志纯[1] 

机构地区：[1]北京科技大学自动化学院北京100083

出　　处：《模式识别与人工智能》2013年第9期812-818,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60973064);中央高校基本科研业务费专项资金项目(No.FRF-TP-12-093A、FRF-SD-12-017A)资助

摘　　要：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.A 2D＋3D multimodal ear recognition method is proposed. Firstly, ear detechon memoo baseo on Adaboost algorithm is used to detect ear part on the 2D images, then the corresponding ear part is located 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 3 D 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 a registered protocol ear, and ear recognition performance is evaluated by the number of the matching points. Finally, Bayes decision rule is used for the decision level fusion of 2D and 3D ear recognition classifiers. The experimental results on the UND ear dataset show the effectiveness of the proposed method. In lighting variation scenario, the proposed 2D ＋3D fusion method outperforms unimodal ear recognition method with 2D images or range images.

关 键 词：人耳识别 3D局部二值模式 决策层融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]