Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Ear Recognition using Improved Non-Negative Matrix Factorization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Recognition Using 3D Ear Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-matcher for ear authentication
Pattern Recognition Letters
Efficient Recognition of Highly Similar 3D Objects in Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Representation for Ear Biometrics
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Fusion of color spaces for ear authentication
Pattern Recognition
Force field feature extraction for ear biometrics
Computer Vision and Image Understanding
Intelligent computing for automated biometrics, criminal and forensic applications
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Toward unconstrained ear recognition from two-dimensional images
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
On guided model-based analysis for ear biometrics
Computer Vision and Image Understanding
Using ear biometrics for personal recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
3-D Ear Modeling and Recognition From Video Sequences Using Shape From Shading
IEEE Transactions on Information Forensics and Security
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Ears have rich structural features that are almost invariant with increasing age and facial expression variations. Therefore ear recognition has become an effective and appealing approach to non-contact biometric recognition. This paper gives an up-to date review of research works on ear recognition. Current 2D ear recognition approaches achieve good performance in constrained environments. However the recognition performance degrades severely under pose, lighting and occlusion. This paper proposes a 2D ear recognition approach based on local information fusion to deal with ear recognition under partial occlusion. Firstly, the whole 2D image is separated to sub-windows. Then, Neighborhood Preserving Embedding is used for feature extraction on each sub-window, and we select the most discriminative sub-windows according to the recognition rate. Each sub-window corresponds to a sub-classifier. Thirdly, a sub-classifier fusion approach is used for recognition with partially occluded images. Experimental results on the USTB ear dataset and UND dataset have illustrated that using only few sub-windows we can represent the most meaningful region of the ear, and the multi-classifier model gets higher recognition rate than using the whole image for recognition.