Abstract
Variance in pose during data acquisition poses a serious challenge for any biometric system which uses the human face as a physiological biometric feature. In this paper, we present an enhanced patchwise fractal dimension based feature extraction technique for the purpose of pose-invariant face recognition. We have presented an improved version of the Differential Box Counting (DBC) based fractal dimension computation technique which is used for feature extraction of thermal images of the human face. A Far-Infrared (FIR) imaging based human face database, called the JU-FIR-F1: FIR Face Database, was developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India for testing the accuracy, stability, and robustness of our proposed feature extraction methodology. We have included the results obtained through extensive experimentation to elaborate the superiority of our proposed algorithm over its other well-known counterparts.
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References
Bowyer, K.W., Hollingsworth, K.P., Flynn, P.J.: A survey of iris biometric research: 2008–2010. In: Bowyer, K.W., Burge, M.J (eds.) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition, pp. 23–61. Springer London, Springer-Verlag London (2016)
Bhowmik, M.K., Saha, K., Majumder, S., Majumder, G., Saha, A., Sarma, A.N., Bhattacharjee, D., Basu, D.K., Nasipuri, M.: Thermal infrared face recognition—a biometric identification technique for robust security system. In: Reviews, Refinements and New Ideas in Face Recognition. InTech (2011)
Chakraborty, S., Bhattacharya, I., Chatterjee, A.: A palmprint based biometric authentication system using dual tree complex wavelet transform. Measurement 46(10), 4179–4188 (2013)
Chakrabarty, A., Jain, H., Chatterjee, A.: Volterra kernel based face recognition using artificial bee colony optimization. Eng. Appl. Artif. Intell. 26(3), 1107–1114 (2013)
Ding, C., Tao, D.: A comprehensive survey on pose-invariant face recognition. ACM Trans. Intell. Syst. Technol. 7(3), 37 (2016)
Galbally, J., Marcel, S., Fierrez, J.: Biometric anti-spoofing methods: a survey in face recognition. IEEE Access 2, 1530–1552 (2014)
Golub, G.H., von Matt, U.: Tikhonov regularization for large scale problems. In: Golub, G.H., Lui, S.H., Luk, F., Plemmons, R. (eds.) Workshop on Scientific Computing, pp. 3–26. Springer (1997)
Joardar, S., Chatterjee, A.: Collaborative representation based face recognition using a hybrid similarity measure with single training sample per person. In: Proceedings of IEEE International Conference on Control, Instrumentation, Energy and Communication (CIEC), pp. 631–635 (2014)
Joardar, S., Chatterjee, A., Rakshit, A.: A real-time palm dorsa subcutaneous vein pattern recognition system using collaborative representation based classification. IEEE Trans. Instrum. Meas. 64(4), 959–966 (2014)
Joardar, S., Chatterjee, A., Rakshit, A.: Real-time NIR imaging of palm dorsa subcutaneous vein pattern based biometrics: an src based approach. IEEE Instrum. Measur. Mag. 19(2), 13–19 (2016)
Joardar, S., Chatterjee, A., Bandyopadhyay, S., Maulik, U.: Multi-size patch based collaborative representation for palm dorsa vein pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm. Eng. Appl. Artif. Intell. 60, 151–163 (2017)
Kouzani, A.Z., He, F., Sammut, K.: Face image matching using fractal dimension. In: Proceedings of International Conference on Image Processing (ICIP 99), vol. 3, pp. 642–646 (1999)
Lai, K., Li, C., He, T., Chen, L., Yu, K., Zhou, W.: Study of an improved differential box-counting approach for gray-level variation of images. In: 10th International Conference on Sensing Technology (ICST), pp. 1–6 (2016)
[online] http://www.sonel.pl/sites/default/files/pl/ins/kt384_ins_104_plgp.pdf
Park, U., Tong, Y., Jain, A.K.: Age-invariant face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 947–954 (2010)
Sarkar, N., Chaudhuri, B.B.: An efficient approach to estimate fractal dimension of textural images. Pattern Recogn. 25(9), 1035–1041 (1992)
Shojaeilangari, S., Yau, W.-Y., Nandakumar, K.: Robust representation and recognition of facial emotions using extreme sparse learning. IEEE Trans. Image Process. 24(7), 2140–2152 (2015)
Sony, S., Singh, A.K.: Survey on methods used in iris recognition system. J. Image Process. Artif. Intell. 3(1) (2017)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: Proceedings of IEEE Conference ICCV 2011, pp. 471–478 (2011)
Zhang, L., Yang, M., Feng, X., Ma, Y., Zhang, D.: Collaborative representation based classification for face recognition. arXiv preprint arXiv:1204.2358 (2012)
Acknowledgements
This work was supported by University Grants Commission (UGC) India under University with Potential for Excellence (UPE)—Phase II Scheme awarded to Jadavpur University, Kolkata, India.
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Joardar, S., Sanyal, A., Sen, D., Sen, D., Chatterjee, A. (2019). An Enhanced Fractal Dimension Based Feature Extraction for Thermal Face Recognition. In: Deep, K., Jain, M., Salhi, S. (eds) Decision Science in Action. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-0860-4_16
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DOI: https://doi.org/10.1007/978-981-13-0860-4_16
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