Biometric Recognition Using 3D Ear Shape

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 8; pp. 1297 - 1308
• Main Authors: Ping Yan, Bowyer, K.W.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.08.2007; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D shape; active contour; Active contours; Applied sciences; Artificial Intelligence; Biometrics; Biometry; Computer science; control theory; systems; Computer systems and distributed systems. User interface; curvature estimation; Ear; ear biometrics; Ear, External - anatomy & histology; Error analysis; Exact sciences and technology; Face recognition; Female; Humans; Image recognition; Image segmentation; Imaging, Three-Dimensional; iterative closest point; Male; Models, Anatomic; Pattern Recognition, Automated; Pattern recognition. Digital image processing. Computational geometry; Preprocessing; Recognition; Shape; Shape recognition; Skin; skin detection; Software; Three dimensional; Biometrics; Computer vision; Image recognition; Image processing; ear biometrics; Iterative method; curvature estimation; Image segmentation; Image matching; 3D shape; Ear; Database; active contour; iterative closest point; Tridimensional image; Skin; Pattern analysis; Curvature; Edge detection; Artificial intelligence; skin detection
• ISSN: 0162-8828; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2007.1067

Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.