The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Face recognition based on convolutional neural network
Open accessAbstract
Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Robust Face Recognition via Multimodal Deep Face Representation
Ding, C., & Tao, D. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2477042 · Cited by 449
Cited by
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Yıldırım, Ö., Pławiak, P., Tan, R.-S., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.009 · Cited by 851
Yildirim, Ö. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.03.016 · Cited by 833
Face Recognition Systems: A Survey
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020) · Sensors · doi:10.3390/s20020342 · Cited by 623
Past, Present, and Future of Face Recognition: A Review
Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020) · Electronics · doi:10.3390/electronics9081188 · Cited by 560
Structural crack detection using deep convolutional neural networks
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.103989 · Cited by 369
Metadata
- Authors
- Coskun, M., Ucar, A., Yildirim, O., & Demir, Y.
- Venue
- 2017 International Conference on Modern Electrical and Energy Systems (MEES)
- Publisher
- IEEE
- Published
- 2017-11
- Pages
- 376-379
- OpenAlex
- W2784158897
- Cited by
- 295
- References
- 6
- FWCI
- 5.34
- Type
- conference paper
- Language
- en
- Open access
- green
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- Coskun, M., Ucar, A., Yildirim, O., & Demir, Y. (2017). Face recognition based on convolutional neural network. 2017 International Conference on Modern Electrical and Energy Systems (MEES). https://doi.org/10.1109/mees.2017.8248937
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