title: 3D ear identification based on sparse representation.
abstract: Biometrics based personal authentication is an effective way for automatically recognizing, with a high confidence, a person's identity. Recently, 3D ear shape has attracted tremendous interests in research field due to its richness of feature and ease of acquisition. However, the existing ICP (Iterative Closet Point)-based 3D ear matching methods prevalent in the literature are not quite efficient to cope with the one-to-many identification case. In this paper, we aim to fill this gap by proposing a novel effective fully automatic 3D ear identification system. We at first propose an accurate and efficient template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common canonical coordinate system determined by the ear contour template, which facilitates much the following stages of feature extraction and classification. For each extracted 3D ear, a feature vector is generated as its representation by making use of a PCA-based local feature descriptor. At the stage of classification, we resort to the sparse representation based classification approach, which actually solves an l1-minimization problem. To the best of our knowledge, this is the first work introducing the sparse representation framework into the field of 3D ear identification. Extensive experiments conducted on a benchmark dataset corroborate the effectiveness and efficiency of the proposed approach. The associated Matlab source code and the evaluation results have been made publicly online available at http://sse.tongji.edu.cn/linzhang/ear/srcear/srcear.htm.
authors.affiliation: School of Software Engineering, Tongji University, Shanghai, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.affiliation: School of Software Engineering, Tongji University, Shanghai, China.
authors.forename: Zhixuan
authors.initials: Z
authors.lastname: Ding
authors.affiliation: School of Software Engineering, Tongji University, Shanghai, China.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Software Engineering, Tongji University, Shanghai, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000295:instrumentation / Q000379:methods*; D004423:Ear / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional; D020407:Internet; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012984:Software
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