Abstract
Recent times are witnessing greater influence of Artificial Intelligence (AI) on identification of subjects based on biometrics. Traditional biometric recognition algorithms, which were constrained by their data acquisition methods, are now giving way to data collected in the unconstrained manner. Practically, the data can be exposed to factors like varying environmental conditions, image quality, pose, image clutter and background changes. Our research is focused on the biometric recognition, through identification of the subject from the ear. The images for the same are collected in an unconstrained manner. The advancements in deep neural network can be sighted as the main reason for such a quantum leap. The primary challenge of the present work is the selection of appropriate deep learning architecture for unconstrained ear recognition. Therefore the performance analysis of various pretrained networks such as VGGNet, Inception Net, ResNet, Mobile Net and NASNet is attempted here. The third challenge we addressed is to optimize the computational resources by reducing the number of learnable parameters while reducing the number of operations. Optimization of selected cells as in NASNet architecture is a paradigm shift in this regard.
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References
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521.7553: 436.
Hatcher, William Grant, and Wei Yu. 2018. A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6: 24411–24432.
Sundararajan, Kalaivani, and Damon L. Woodard. (2018). Deep learning for biometrics: A survey. ACM Computing Surveys (CSUR) 51 (3): 65.
Kumar, Ajay, and Wu Chenye. 2012. Automated human identification using ear imaging. Pattern Recognition 45 (3): 956–968.
Nejati, Hossein, Li Zhang, Terence Sim, Elisa Martinez-Marroquin, and Guo Dong. (2012). Wonder ears: Identification of identical twins from ear images. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 1201–1204. IEEE.
Emersic, Ziga, Dejan Stepec, Vitomir Struc, Peter Peer, Anjith George, Adii Ahmad, Elshibani Omar et al. 2017. The unconstrained ear recognition challenge. In 2017 IEEE International Joint Conference on Biometrics (IJCB), 715–724. IEEE.
Emersic, Ziga, B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo et al. 2019. The unconstrained ear recognition challenge 2019-arxiv version with appendix. arXiv:1903.04143.
Alom, Md Zahangir, et al. 2018. The history began from AlexNet: A comprehensive survey on deep learning approaches. arXiv:1803.01164.
Unnikrishnan, Anju, V. Sowmya, and K. P. Soman. 2019. Deep learning architectures for land cover classification using red and near-infrared satellite images. Multimedia Tools and Applications 1–16.
LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11): 2278–2324.
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 1097–1105.
Zeiler, Matthew D., and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In European Conference on Computer Vision, 818–833. Springer, Cham.
Lin, Min, Qiang Chen, and Shuicheng Yan. 2013. Network in network. arXiv:1312.4400.
Simonyan, Karen, and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
Larsson, Gustav, Michael Maire, and Gregory Shakhnarovich. 2016. Fractalnet: Ultra-deep neural networks without residuals. arXiv:1605.07648.
Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
Howard, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861.
Chollet, François. 2017. Xception: Deep learning with depthwise separable-convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258.
Zoph, Barret, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le. 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 8697–8710.
Kurup, R. Vimal, V. Sowmya, and K. P. Soman. 2019. Effect of data pre-processing on brain tumor classification using capsulenet. In International Conference on Intelligent Computing and Communication Technologies 110–119. Singapore:Springer.
Sabour, Sara, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. In Advances in Neural Information Processing Systems 3856–3866.
Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2818–2826.
K. He, X. Zhang, S. Ren, J. Sun. 2016. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778.
Chen, Yukang, Qian Zhang, Chang Huang, Mu Lisen, Gaofeng Meng, and Xinggang Wang. 2018. Reinforced evolutionary neural architecture search. CoRR.
Emersic, Žiga, Blaž Meden, Peter Peer, and Vitomir Štruc. 2018. Evaluation and analysis of ear recognition models: Performance, complexity and resource requirements. Neural Computing and Applications 1–16.
Zhang, Yi, and Zhichun Mu. 2017. Ear detection under uncontrolled conditions with multiple scale faster region-based convolutional neural networks. Symmetry 9 (4): 53.
Emersic, Z., V. Struc, and P. Peer. 2017. Ear recognition: More than a survey. Neurocomputing 255: 26–39.
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Radhika, K., Devika, K., Aswathi, T., Sreevidya, P., Sowmya, V., Soman, K.P. (2020). Performance Analysis of NASNet on Unconstrained Ear Recognition. In: Rout, M., Rout, J., Das, H. (eds) Nature Inspired Computing for Data Science. Studies in Computational Intelligence, vol 871. Springer, Cham. https://doi.org/10.1007/978-3-030-33820-6_3
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