IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Recognition Using 3D Ear Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for ICP-based 3D shape biometrics
Computer Vision and Image Understanding
A multi-matcher for ear authentication
Pattern Recognition Letters
A Fast and Fully Automatic Ear Recognition Approach Based on 3D Local Surface Features
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Fusion of color spaces for ear authentication
Pattern Recognition
Efficient Detection and Recognition of 3D Ears
International Journal of Computer Vision
Further developments in geometrical algorithms for ear biometrics
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Using ear biometrics for personal recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
A rotation and scale invariant technique for ear detection in 3D
Pattern Recognition Letters
Multibiometric human recognition using 3D ear and face features
Pattern Recognition
Rigid and non-rigid shape matching for mechanical components retrieval
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
ACM Computing Surveys (CSUR)
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Ear is a new class of relatively stable biometric that is invariant from childhood to early old age (8 to 70). It is not affected with facial expressions, cosmetics and eye glasses. In this paper, we introduce a two-step ICP (Iterative Closest Point) algorithm for matching 3D ears. In the first step, the helix of the ear in 3D images is detected. The ICP algorithm is run to find the initial rigid transformation to align a model ear helix with the test ear helix. In the second step, the initial transformation is applied to selected locations of model ears and the ICP algorithm iteratively refines the transformation to bring model ears and test ear into best alignment. The root mean square (RMS) registration error is used as the matching error criterion. The model ear with the minimum RMS error is declared as the recognized ear. Experimental results on a dataset of 30 subjects with 3D ear images are presented to demonstrate the effectiveness of the approach.