Abstract
Face recognition is not a new phenomenon, but still there is a lot which can be done. We are aiming in this work to develop an improved face recognition system for person-dependent and person-independent variants. To extract relevant facial features, we are using the convolutional neural network. These features allow comparing faces of different subjects in an optimized manner. The system training module, firstly recognizes different subjects of dataset, in another approach, the module processes a different set of new images. Thus, the predictions improve on previously trained data. The accuracy on Yale dataset of 15 people, 11 images per person with a total of 165 images, yielded 96.19% accurate results; the system can be easily scaled for more images and different datasets.
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References
T. Priya, J. Sarika, IJournals: Int. J. Softw. Hardware Res. Eng. 5(9) ISSN-2347-4890 (2017)
A. Krizhevsky, I. Sutskever, G.E. Hinton. Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
M. Sherif, M. Azab, Y. Emad, S. Sameh, F. Menisy, B. Kandil, Eyes in the sky, in Companion Proceedings of the 10th International Conference on Utility and Cloud Computing–UCC ‘17 Companion (2017)
S.B. Lo, H. Li, Y. Wang, L. Kinnard, M.T. Freedman, A multiple circular path convolution neural network system for detection of mammographic masses. IEEE Trans. Med. Imag. 150–158 (2002)
Y.H. Hu, J. Hwang, Handbook of Neural Network Signal Processing, CRC Press. Gepperth A. (ed.) Object detection and feature base learning by sparse convolutional neural networks, Lecture notes in artificial intelligence 4807 (Springer, Berlin, 2002), pp. 221–231
H. Khalajzadeh, M. Mansouri, M. Teshnehlab, Face Recognition using Convolutional Neural Network and Simple Logistic Classifier. WSC17 (2017)
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Tambi, P., Jain, S., Mishra, D.K. (2019). Person-Dependent Face Recognition Using Histogram of Oriented Gradients (HOG) and Convolution Neural Network (CNN). In: Kamal, R., Henshaw, M., Nair, P. (eds) International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol 870. Springer, Singapore. https://doi.org/10.1007/978-981-13-2673-8_5
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DOI: https://doi.org/10.1007/978-981-13-2673-8_5
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