TY - GEN
T1 - Ear recognition via sparse representation and Gabor filters
AU - Khorsandi, Rahman
AU - Cadavid, Steven
AU - Abdel-Mottaleb, Mohamed
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this paper, we present a fully automated approach for ear recognition based upon sparse representation. In sparse representation, features extracted from the training data of each subject are used to develop a dictionary. In this work, Gabor filters are used for feature extraction. Classification is performed by extracting features from the test data and using the dictionary for representing the test data. The class of the test data is then determined based upon the involvement of the dictionary entries in its representation. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing large appearance, pose, and lighting variability, yielded a rank-one recognition rate of 98.46%. The proposed system outperforms the method described in [1], which achieves a recognition rate of 96.88% when evaluated on the same dataset. Moreover, the proposed system was evaluated on a greater number of test images per subject, demonstrating its robustness.
AB - In this paper, we present a fully automated approach for ear recognition based upon sparse representation. In sparse representation, features extracted from the training data of each subject are used to develop a dictionary. In this work, Gabor filters are used for feature extraction. Classification is performed by extracting features from the test data and using the dictionary for representing the test data. The class of the test data is then determined based upon the involvement of the dictionary entries in its representation. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing large appearance, pose, and lighting variability, yielded a rank-one recognition rate of 98.46%. The proposed system outperforms the method described in [1], which achieves a recognition rate of 96.88% when evaluated on the same dataset. Moreover, the proposed system was evaluated on a greater number of test images per subject, demonstrating its robustness.
KW - Ear Recognition
KW - Feature extraction
KW - Gabor Filters
KW - Sparse Representation
UR - http://www.scopus.com/inward/record.url?scp=84871971973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871971973&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2012.6374589
DO - 10.1109/BTAS.2012.6374589
M3 - Conference contribution
AN - SCOPUS:84871971973
SN - 9781467313841
T3 - 2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012
SP - 278
EP - 282
BT - 2012 IEEE 5th International Conference on Biometrics
T2 - 2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012
Y2 - 23 September 2012 through 27 September 2012
ER -