Abstract
The objective of this research is to study the effect of eyeglasses and the masking of the eye portion on the recognition accuracy of the periocular biometric authentication system. In this paper, six different off-the-shelf deep Convolutional Neural Networks (CNN) are implemented. Experimental results show that in both the cases VGG 19 CNN model outperforms others on the UBIPr database.
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References
Mahalingam, G., Ricanek, K.: LBP-based periocular recognition on challenging face datasets. EURASIP J. Image Video Process. 1, 1–36 (2013). https://doi.org/10.1186/1687-5281-2013-36
Park, U., Jillela, R.R., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forensics Secur. 6(1), 96–106 (2011). https://doi.org/10.1109/TIFS.2010.2096810
A comprehensive hands-on guide to transfer learning with real-world applications in deep learning. https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
Oh, B.S., Oh, K., Toh, K.A.: On projection-based methods for periocular identity verification In: International Conference on Industrial Electronics and Applications, pp. 871–876. Singapore (2012). https://doi.org/10.1109/iciea.2012.6360847
Uzair, M., Mahmood, A., Mian, A. et al.: Periocular biometric recognition using image sets. In: IEEE Workshop on Applications of Computer Vision, pp. 246– 251. Tampa, FL, USA (2013). https://doi.org/10.1109/wacv.2013.6475025
Chen, H., Gao, M., Ricanek, K., Xu, W., et al.: A novel race classification method based on periocular features fusion. Int. J. Pattern Recognit Artif Intell. 31(8), 1–21 (2017). https://doi.org/10.1142/S0218001417500264
Lyle, J., Miller, P., Pundlik, S. et al.: Soft biometric classification using periocular region features. In: IEEE International Conference on Biometrics: Theory Applications and Systems, pp. 1–7. Washington, DC, USA (2010). https://doi.org/10.1109/btas.2010.5634537
Boddeti, V., Smereka, J., Kumar, B.: A comparative evaluation of iris and ocular recognition methods on challenging ocular images. In: IEEE International Joint Conference on Biometrics, pp. 10–18. Washington, DC, USA (2011). https://doi.org/10.1109/ijcb.2011.6117500
Zhao, Z., Kumar, A.: Improving periocular recognition by explicit attention to critical regions in deep neural network. IEEE Trans. Inform. Frensics Secur. 13(12), 1–15 (2018). https://doi.org/10.1109/TIFS.2018.2833018
Alahmadi, M., Hussain, H., Aboalsamh, et al.: Convsrc: Smartphone based peri ocular recognition using deep convolutional neural network and sparsity augmented collaborative representation (2018). https://doi.org/10.1016/j.patcog.2016.12.017, arXiv:1801.05449
Tapia, J., Aravena Carlos C.: Gender Classification from periocular NIR images using fusion of CNNs models. In: IEEE International Conference on Identity, Security, and Behavior Analysis, pp. 1–6. Singapore (2018). https://doi.org/10.1109/isba.2018.8311465
Keshari, R., Ghosh, S., Agarwal, A., et al.: Mobile periocular matching with pre- Post cataract surgery. In: IEEE International Conference on Image Processing, pp. 1–6. Phoenix, AZ, USA (2016). https://doi.org/10.1109/icip.2016.7532933
Zhao, Z., Kumar, A.: Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network. IEEE Trans. Inf. Forensics Secur. 12(5), 1017–1030 (2017). https://doi.org/10.1109/TIFS.2016.2636093
UBIPr http://socia-lab.di.ubi.pt/~ubipr/. Last accessed 19 May 2019
Liu, P., Jing-Ming Guo, J.-M., et al.: Ocular recognition for blinking eyes. IEEE Trans. Image Process. 26(10), 5070–5081 (2017). https://doi.org/10.1109/TIP.2017.2713041
Hussain, M., Bird, J.J., Faria, D.R.: A study on CNN transfer learning for im age classification. In: Workshop on Computational Intelligence, pp. 191–202. Nottingham, Springer, Berlin (2018). https://doi.org/10.1007/978-3-319-97982-3_16
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2012). https://doi.org/10.1145/3065386
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. Boston, MA, USA (2015). https://doi.org/10.1109/cvpr.2015.7298594
He, K., Zhang, X., Ren, S. et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770– 778. Las Vegas, NV, USA (2016). https://doi.org/10.1109/cvpr.2016.90
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
Miller P.E., Rawls, A.W., Pundlik, S.J., Woodard, D.L.: Personal identification using periocular skin texture. In: ACM Symposium on Applied Computing, pp. 1496–1500 ACM (2010). https://doi.org/10.1145/1774088.1774408
Nie, L., Kumar, A., Zhan, S.: Periocular recognition using unsupervised convolutional RBM feature learning. In: IEEE International Conference on Pattern Recognition, pp. 399–404. Stockholm, Sweden (2014). https://doi.org/10.1109/icpr.2014.77
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Kumari, P., R., S.K. (2020). Periocular Biometrics for Non-ideal Images Using Deep Convolutional Neural Networks. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_15
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