One of the challenges posed by technological advancements in modern society is the issue of identity verification and authentication. Among various biometric identification methods, ear biometrics is a relatively new approach. Identity recognition has garnered significant attention in the realm of biometrics, particularly with the advancement of image processing techniques. Among various biometric traits, ear recognition is emerging as a reliable method due to the unique structure of human ears. The human ear possesses unique characteristics that make it suitable for identity recognition. In this study, we employ Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) operators for pattern recognition in ear images. To reduce the feature vector size and enhance classifier accuracy, we apply Principal Component Analysis (PCA) to the features extracted using LBP. Finally, we utilize the k-Nearest Neighbors (k-NN) algorithm with the Canberra similarity measure for classification. To evaluate the efficiency of our proposed method, we conducted experiments on the USTB-1 database, which contains 180 images from 60 individuals, achieving an accuracy of 98.33%.
Burge, M., & Burger, W. (2001). Ear biometrics for machine vision. Johannes Kepler University Department of Systems Science Computer Vision Laboratory.
Iannarelli, A. V. (1989). Ear identification. Paramont Publishing Company.
Choras, M. (2005). Ear biometrics based on geometrical feature extraction. Electronic Letters on Computer Vision and Image Analysis, 5(3), 84–95. https://doi.org/10.5565/rev/elcvia.108
Kumar, V. K., & Srinivasan, B. (2012). Ear biometrics in human identification system. I.J. Information Technology and Computer Science, 5(2), 41–47. https://doi.org/10.5815/ijitcs.2012.02.06
Hurley, D. J., Arbab-Zavar, B., & Nixon, M. S. (2007). The ear as a biometric. European Association for Signal Processing, 25–29.
Zhang, H., Mu, Z., Qu, W., Liu, L., & Zhang, C. (2005). A novel approach for ear recognition based on ICA and RBF network. Proceedings of the 4th International Conference on Machine Learning and Cybernetics, 18–21.
Ansari, S., & Gupta, P. (2007). Localization of ear using outer helix curve of the ear. Proceedings of the International Conference on Computing: Theory and Applications, 688–692. https://doi.org/10.1109/ICCTA.2007.82
Zhang, H., & Mu, Z. (2008). Compound structure classifier system for ear recognition. Proceedings of the International Conference on Automation and Logistics, 2306–2309. https://doi.org/10.1109/ICAL.2008.4636551
Alaraj, M., Hou, J., & Fukami, T. (2010). A neural network based human identification framework using ear images. TENCON-IEEE Region 10 Conference, 1595–1600. https://doi.org/10.1109/TENCON.2010.5686043
Yazdanpanah, A. P., & Faez, K. (2011). Gabor-based RCM features for ear recognition. In J. Yang (Ed.), State of the art in biometrics (pp. 221–234). Intech.
Annapurani, K., Sadiq, M. A. K., & Malathy, C. (2015). Fusion of shape of the ear and tragus—A unique feature extraction method for ear authentication system. Expert Systems with Applications, 42(1), 649–656. https://doi.org/10.1016/j.eswa.2014.08.009
Ghoualmi, L., Draa, A., & Chikhi, S. (2016). An ear biometric system based on artificial bees and the scale invariant feature transform. Expert Systems with Applications, 57, 49–61. https://doi.org/10.1016/j.eswa.2016.03.004
Ojansivu, V., & Heikkila, J. (2008). Blur insensitive texture classification using local phase quantization. Image and Signal Processing, 236–243. https://doi.org/10.1007/978-3-540-69905-7_27
Ojala, T., Pietikainen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59. https://doi.org/10.1016/0031-3203(95)00067-4
Guo, Z., Zhang, L., & Zhang, D. (2010). A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing, 19, 1657-1663.
Fathi,A. and Sajjadi,S. (2021). Identity Recognition from Ear Images Using Local Binary Pattern and Local Phase Quantization. Transactions on Machine Intelligence, 4(3), 128-136. doi: 10.47176/TMI.2021.128
MLA
Fathi,A. , and Sajjadi,S. . "Identity Recognition from Ear Images Using Local Binary Pattern and Local Phase Quantization", Transactions on Machine Intelligence, 4, 3, 2021, 128-136. doi: 10.47176/TMI.2021.128
HARVARD
Fathi A., Sajjadi S. (2021). 'Identity Recognition from Ear Images Using Local Binary Pattern and Local Phase Quantization', Transactions on Machine Intelligence, 4(3), pp. 128-136. doi: 10.47176/TMI.2021.128
CHICAGO
A. Fathi and S. Sajjadi, "Identity Recognition from Ear Images Using Local Binary Pattern and Local Phase Quantization," Transactions on Machine Intelligence, 4 3 (2021): 128-136, doi: 10.47176/TMI.2021.128
VANCOUVER
Fathi A., Sajjadi S. Identity Recognition from Ear Images Using Local Binary Pattern and Local Phase Quantization. Trans. Mach. Intell., 2021; 4(3): 128-136. doi: 10.47176/TMI.2021.128
