A NOVEL 3D EAR IDENTIFICATION APPROACH BASED ON SPARSE REPRESENTATION

Recently, ear shape has attracted tremendous interests in biometric research due to its richness of feature and ease of acquisition. In this paper, we present a novel 3D ear identification approach based on the sparse representation framework. To this end, at first, we propose a template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common standard coordinate system determined by the template, which facilitates the following feature extraction and classification. For each 3D ear, a feature vector can be generated as its representation. With respect to the ear identification, we resort to the l(1)-minimization based sparse representation. Experiments conducted on a benchmark dataset corroborate the effectiveness and efficacy of the proposed approach.