Ear Recognition with Variant Poses Using Locally Linear Embedding
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
A New Segmentation Approach for Ear Recognition
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
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
Ear recognition based on local information fusion
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Robust ear based authentication using Local Principal Independent Components
Expert Systems with Applications: An International Journal
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An Improved Non-Negative Matrix Factorization with sparseness constraints (INMFSC) is proposed by imposing an additional constraint on the objective function of NMFSC, which can control the sparseness of both the basis vectors and the coefficient matrix simultaneously. The update rules to solve the objective function with constraints are presented. Research of ear recognition and its application is a new subject in the field of biometrics authentication. In practical application, ear is maybe partially occluded by hair etc. So the proposed INMFSC is applied on ear recognition with normal images and partially occluded images. Experiment results show that, compared with the traditional NMFSC, the proposed method not only obtains higher recognition rate, but also improves the sparseness and the orthogonality of coefficient matrix.