Ear recognition is an active area of research and automatic ear recognition is one of the challenging areas in biometric and forensic domains. Human ear contains large amount of unique features for recognition of an individual. There are different approaches and descriptors that achieve relatively good results in ear biometric recognition. Studies show that there is poor recognition performance in case of occlusion, illumination variation and pose variation. This paper presents an overview of different local texture descriptors in the field of automatic ear recognition. The local descriptors which calculate features from small local patches have proven to be more effective in real world situations compared to the global descriptors which extract features from whole image.

Ear, Biometric, Texture Descriptors, Feature Extraction, LBP, GLCM, LPQ

[1] Ear biometrics: A survey of detection, Emersic, Z., Struc, V., & Peer, P. (2017). Ear recognition: More than a survey. Neurocomputing, 255, 26-39.
[2] A. Iannarelli. Ear identification. Forensic Identification Series. Paramont publishing company, Fremont,California,1989.
[3] A. Kumar, C. Wu, Automated human identification using ear imaging, Pattern Recognition(2011) ,doi:10.1016/j.patcog.2011.06.005 .
[4] Choras M. (2004) Human Ear Identification Based on Image Analysis. In: Rutkowski L., Siekmann J.H., Tadeusiewicz R., Zadeh L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science, vol 3070. Springer, Berlin, Heidelberg..
[5] Bertillon A. Identification Anthropometrique: Instructions Signaletique; 1885.
[6] M. Burge, W. Burger, Biometrics: Personal Identification in Networked Society, Springer US, Boston, MA, 1996, Ch. Ear Biometrics, pp. 273–285.
[7] B. Moreno, A. Sánchez, J. F. Vélez, On the use of outer ear images for personal identification in security applications, in: Proceedings of the International Carnahan Conference on Security Technology, IEEE, 1999, pp. 469–476.
[8] Z. Mu, L. Yuan, Z. Xu, D. Xi, S. Qi, Shape and structural feature based ear recognition, in: Advances in biometric person authentication, Springer, 2004, pp. 663–670.
[9] M. Choras, R. S. Choras, Geometrical algorithms of ear contour shape representation and feature extraction, in: Proceedings of the International Conference on Intelligent Systems Design and Applications, IEEE, 2006, pp. 451–456.
[10] D. J. Hurley, M. S. Nixon, J. N. Carter, Automatic ear recognition by force field transformations, in: Proceedings of the Colloquium on Visual Biometrics, IET, 2000, pp. 7–1.
[11] B. Victor, K. Bowyer, S. Sarkar, An evaluation of face and ear biometrics, in: Proceedings of the International Conference on Pattern Recognition, Vol. 1, IEEE, 2002, pp. 429–432.
[12] K. Chang, K. W. Bowyer, S. Sarkar, B. Victor, Comparison and combination of ear and face images in appearance-based biometrics, Transactions on Pattern Analysis and Machine Intelligence 25 (9) (2003) 1160–1165.
[13] H.-J. Zhang, Z.-C. Mu, W. Qu, L.-M. Liu, C.-Y. Zhang, A novel approach for ear recognition based on ICA and RBF network, in: Proceedings of the International Conference on Machine Learning and Cybernetics, Vol. 7, IEEE, 2005, pp. 4511–4515.
[14] L. Nanni, A. Lumini, Fusion of color spaces for ear authentication,Pattern Recognition 42 (9) (2009) 1906–1913.
[15] A. Pflug, C. Busch, A. Ross, 2D ear classification based on unsupervised clustering, in: Proceedings of the International Joint Conference on Biometrics, IEEE, 2014, pp. 1–8.
[16] A. Benzaoui, N. Hezil, A. Boukrouche, Identity recognition based on the external shape of the human ear, in: Proceedings of the International Conference on Applied Research in Computer Science and Engineering, IEEE, 2015, pp. 1–5.
[17] A. Pflug, P. N. Paul, C. Busch, A comparative study on texture and surface descriptors for ear biometrics, in: Proceedings of the International Carnahan Conference on Security Technology, IEEE, 2014, pp. 1–6.
[18] L. Jacob, G. Raju, Advances in Signal Processing and Intelligent Recognition Systems, Springer International Publishing,Cham, 2014, Ch. Ear Recognition Using Texture Features – A Novel Approach, pp. 1–12.
[19] Ojala, T. and Pietikäinen, M. (1999), Unsupervised Texture Segmentation Using Feature Distributions. Pattern Recognition 32:477-486.
[20] V. Ojansivu and J. Heikkil, “Blur insensitive texture classification using local phase quantization,” in Proc. 3rd Int. Conf. on Image and SignalProcessing (ICSIP), pp. 236–243, Springer–Verlag, Berlin, Heidelberg(2008).
[21] J. Kannala and E. Rahtu, “BSIF: binarized statistical image features,” inProc. IEEE Int. Conf. on Pattern Recognition (ICPR), pp. 1363–1366,IEEE, Tsukuba, Japan (2012).
[22] T.-S. Chan, A. Kumar, Reliable ear identification using 2-D quadrature filters, Pattern Recognition Letters 33 (14) (2012)1870–1881.
[23] A. Kumar, T.-S. T. Chan, Robust ear identification using sparse representation of local texture descriptors, Pattern recognition 46 (1) (2013) 73–85.
[24] A. Basit, M. Shoaib, A human ear recognition method using nonlinear curvelet feature subspace, International Journal of Computer Mathematics 91 (3) (2014) 616–624.
[25] A. Benzaoui, A. Hadid, A. Boukrouche, Ear biometric recognition using local texture descriptors, Journal of Electronic Imaging 23 (5) (2014) 053008.
[26] A. Benzaoui, A. Kheider, A. Boukrouche, Ear description and recognition using ELBP and wavelets, in: Proceedings of the International Conference on Applied Research in Computer Science and Engineering, 2015, pp. 1–6.
[27] A. Benzaoui, N. Hezil, A. Boukrouche, Identity recognition based on the external shape of the human ear, in: Proceedings of the International Conference on Applied Research in Computer Science and Engineering, IEEE, 2015, pp. 1–5.
[28] H. Bourouba, H. Doghmane, A. Benzaoui, A. H. Boukrouche, Ear recognition based on Multi-bags-of-features histogram,in: Proceedings of the International Conference on Control,Engineering Information Technology, 2015, pp. 1–6.
[29] A. Meraoumia, S. Chitroub, A. Bouridane, An automated ear identification system using Gabor filter responses, in: Proceedings of the International Conference on New Circuits and Systems, IEEE, 2015, pp. 1–4.
[30] Z. Youbi et al., “Human ear recognition based on multi-scale local binary pattern descriptor and KL divergence,” in Proc. of the 39th IEEE Int. Conf. on Telecommunications and Signal Processing (TSP), pp. 685–688 (2016).
[31] Amir Benzaoui, InsafAdjabi, AbdelhaniBoukrouche, “Experiments and improvements of ear recognition based on local texture descriptors,” Opt. Eng. 56(4), 043109 (2017).