Color image enhancement by fuzzy intensification
Pattern Recognition Letters
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
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AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Ear Recognition by means of a Rotation Invariant Descriptor
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Ear Recognition using Improved Non-Negative Matrix Factorization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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Force field feature extraction for ear biometrics
Computer Vision and Image Understanding
On shape-mediated enrolment in ear biometrics
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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
Efficient Detection and Recognition of 3D Ears
International Journal of Computer Vision
Automated human identification using ear imaging
Pattern Recognition
Reliable ear identification using 2-D quadrature filters
Pattern Recognition Letters
| Hi-index | 12.05 |
This paper presents the ear based authentication using Local Principal Independent Components (LPIC) an extension of PCA. As PCA is a global approach dealing with all pixel intensities, it is difficult to get finer details from the ear image. The concept of information sets is introduced in this paper so as to have leverage over the local information. These sets are based on the granularization of the ear image in the form of windows. The features based on these sets allow us to change the local information which goes into LPIC as the input. Thus LPIC not only uses this local information but also helps to reduce the dimensions of the deduced features far less than that can be achieved with PCA. For the extraction of sparse information from ear, features such as Effective information (EI), Energy feature (EF), Sigmoid feature (SF), Multi Quadratic feature (MQD) are derived and then LPIC is applied to get the reduced number of features. Inner product classifier (IPC) is developed for the classification of these features. The experiments carried out on constrained and unconstrained databases show that LPIC is effective not only under the ideal conditions but also under the unconstrained environment.