Human recognition using 3D ear images

• Published in: Neurocomputing (Amsterdam) Vol. 140; pp. 317 - 325
• Main Authors: Prakash, Surya, Gupta, Phalguni
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 22.09.2014; Elsevier
• Subjects: 2D and 3D Ear Recognition; Alignment; Applied sciences; Artificial intelligence; Biometrics; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Ear; Exact sciences and technology; Generalized Procrustes Analysis; Iterative Closest Point Technique; Local Feature Points; Matching; Object recognition; Pattern recognition. Digital image processing. Computational geometry; Software; Three dimensional; Three dimensional models; Two dimensional; Biometrics; Local Feature Points; Generalized Procrustes Analysis; 2D and 3D Ear Recognition; Iterative Closest Point Technique; Image registration; Computer vision; Image recognition; Error rate; Posture; Image matching; Ear; Tridimensional image; Procrustes analysis
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.03.007

This paper proposes an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to compare two 3D ears. In the first step, it computes salient 3D data points from 3D ear images with the help of local 2D feature points of co-registered 2D ear images. Subsequently, it uses these salient 3D points to coarsely align 3D ear images. In the second step, it performs final matching of coarsely aligned 3D ear images by using a Generalized Procrustes Analysis (GPA) and Iterative Closest Point (ICP) based matching technique (GPA-ICP). The proposed technique has been tested on 1780 images of 404 subjects (two or more images per subject) of University of Notre Dame public database-Collection J2 (UND-J2) which consists of co-registered 2D and 3D ear images with scale and pose variations. It has achieved a verification accuracy of 98.30% with an equal error rate of 1.8%. •Human recognition using 3D along with co-registered 2D ear images.•Use of local 2D feature points for initial 3D data alignment.•Use of Generalized Procrustes Analysis and ICP based technique for robust 3D data matching.•The technique can be used in many applications such as face and object recognition.