Face Recognition Using Principal Component Analysis, K-Means Clustering, and Convolutional Neural Network
Face recognition is the problem of identifying or recognizing individuals in an image. This paper investigates a possible method to bring a solution to this problem. The method proposes an amalgamation of Principal Component Analysis (PCA), K-Means clustering, and Convolutional Neural Network (CNN) for a face recognition system. It is trained and evaluated using the ORL dataset. This dataset consists of 400 different faces with 40 classes of 10 face images per class. Firstly, PCA enabled the usage of a smaller network. This reduces the training time of the CNN. Thus, we get rid of the redundancy and preserve the variance with a smaller number of coefficients. Secondly, the K-Means clustering model is trained using the compressed PCA obtained data which select the K-Means clustering centers with better characteristics. Lastly, the K-Means characteristics or features are an initial value of the CNN and act as input data. The accuracy and the performance of the proposed method were tested in comparison to other Face Recognition (FR) techniques namely PCA, Support Vector Machine (SVM), as well as K-Nearest Neighbour (kNN). During experimentation, the accuracy and the performance of our suggested method after 90 epochs achieved the highest performance: 99% accuracy F1-Score, 99% precision, and 99% recall in 463.934 seconds. It outperformed the PCA that obtained 97% and KNN with 84% during the conducted experiments. Therefore, this method proved to be efficient in identifying faces in the images.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 378
 S.V.A.V.Prasad & Shilpi Singha, 2018, ´ techniques and challenges of face recognition: a critical review ´, Procedia Computer Science, no.143, pp.536–543.
 Bhaskar, B., Anushree, P.S., Shree, S.D. and Prashanth, K.V.M. (2015). Quantitative Analysis of Kernel Principal Components and Kernel Fishers Based Face Recognition Algorithms Using Hybrid Gaborlets. Procedia Computer Science, vol 58, pp.342–347.
 Vahid Mirjalili and Sebastian Raschka. Python Machine Learning. Packt Publishing Ltd, UK Birmingham, 2017.
 Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun,Y. What is the best multi-stage architecture for ob-ject recognition? In Proc. International Conference on Computer Vision (ICCV’09). IEEE, 2009.
 Vinod Nair and Geoffrey E. Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines, Department of Computer Science, University of Toronto, Toronto, ON M5S 2G4, Canada, 2010.
 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. and Bengio, Y. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research,15 (56):1929-1958.
 Long, J., Shelhamer, E. and Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440.
 Viola Paul and Michael J. Jones. Robust real-time face detection. International journal of computer vision 57, no. 2 (2004): 137-154, 2004
 Ren, S., He, K., Girshick, R. and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems, vol 28, pp. 91-99, 2015.
 Prateek Joshi, Artificial Intelligence with Python, Packt Publishing Ltd, Birmingham, UK, 2017, p. 408.
 Sebastian Raschka & Vahid Mirjalili, Python Machine Learning, Packt Publishing Ltd, Birmingham, UK, 2017, p. 12.
 Richard Ernest Bellman, Dynamic Programming, Princeton University Press, Rand Corporation, 1957, p. ix.
 Alaa Eleyan and Hasan Demirel. Face Recognition System Based on PCA and Feedforward Neural Networks, Department of Electrical Engineering and Electronic, Eastern Mediterranean University, Gazimagusa, North Cyprus, Mersin 10, Turkey, 2014.
 Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classification, Department of Computer Science, National Taiwan University, Taipei 106, Taiwan, 2016, p. 4.
 Zhao, F., Li, J., Zhang, L., Li, Z. and Na, S.-G. (2020). Multi-view face recognition using deep neural networks. Future Generation Computer Systems, vol 111, pp.375–380.
 E. I. Abbas, M. E. Safi and K. S. Rijab, 2017, ´Face recognition rate using different classifier methods based on PCA`, International Conference on Current Research in Computer Science and Information Technology (ICCIT), 2017, pp. 37-40