A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-matcher for ear authentication
Pattern Recognition Letters
Fusion of color spaces for ear authentication
Pattern Recognition
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Robust palmprint verification using 2D and 3D features
Pattern Recognition
Using ear biometrics for personal recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Multimodal ear recognition based on 2d+3d feature fusion
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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Multi-biometric 2D and 3D ear recognition are explored. The data set used represents over 300 persons, each with images acquired on at least two different dates. Among them, 169 persons have images taken on at least four different dates. Based on the results of three algorithms applied on 2D and 3D ear data, various multi-biometric combinations were considered, and all result in improvement over a single biometric. A new fusion rule using the interval distribution between rank 1 and rank 2 outperforms other simple fusion rules. In general, all the approaches perform better with multiple representations of a person.