Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition
Downloads
Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.
Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.
Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).
Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.
Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers.
Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning
A.-E. C. Pandelea, G. Covatariu and M. Budescu, "Image Processing Using Artificial Neural Networks," Bulletin of the Polytechnic Institute of Jassy, Section, no. Constructions Architecture, 2015.
O. I. Abiodun, K. V. Dada, A. Jantan, A. E. Omolara, H. Arshad, A. M. Umar, O. U. Linus, M. U. Kiru, A. A. Kazaure and U. Gana, "Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition," IEEE Access, vol. 7, pp. 158820-158846, 2019.
A. Wosiak, A. Zamecznik and K. N. Jarosik, "Supervised and unsupervised machine learning for improved identification of intrauterine growth restriction types," in Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland, 2016.
M. Seetha, I. V. Muralikrishna, B. L. Deekshatulu and B. L. Malleswari, "Artificial Neural Networks and Other Methods of Image Classification," Journal of Theoretical and Applied Information Technology (JATIT), pp. 1039-1053, 2013.
A. Olaode, G. Naghdy and C. Todd, "Unsupervised Classification of Images: A Review," International Journal of Image Processing (IJIP), vol. VIII, no. 5, pp. 325-342, 2014.
R. A. Priyanka, C. Ashwitha, R. A. Chakravarthi and R. Prakash, "Face Recognition Model Using Back Propagation," International Journal of Engineering & Technology, 2018.
Q. Huang and L. Cui, "Design and Application of Face Recognition Algorithm Based on Improved Backpropagation Neural Network," Revue d'Intelligence Artificielle, vol. 33, no. 1, pp. 25-32, 2019.
P. Bose and S. K. Bandhyopadhyay, "Human Face Detection: Manual vs. Kohonen Self Organizing Map," 11 September 2020. [Online]. Available: https://www.preprints.org/.
V. Skuratov, K. Kuzmin, I. Nelin and M. Sedankin, "Application Of Kohonen Self Organizing Map to Search for Region of Interest in the Detection Interest in The Detection Of Objects," EUREKA: Physics and Engineering, 2020.
ihritik, "RGB Image to Grayscale Image Conversion," GeeksforGeeks, 25 June 2018. [Online]. Available: https://www.geeksforgeeks.org/matlab-rgb-image-to-grayscale-image-conversion/. [Accessed 18 September 2021].
KAGGLE, "The ORL database for training and testing: The Olivetti Research Laboratory (ORL) face dataset," KAGGLE, 2020. [Online]. Available: https://www.kaggle.com/tavarez/the-orl-database-for-training-and-testing. [Accessed 16 September 2021].
D. Gupta, "Fundamentals of Deep Learning – Activation Functions and When to Use Them?," Analytics Vidhya, 30 January 2020. [Online]. Available: https://www.analyticsvidhya.com/blog/2020/01/fundamentals-deep-learning-activation-functions-when-to-use-them/. [Accessed 2021 September 18].
L. S. Moonlight and A. S. Prabowo, "Forecasting System for Passenger, Airplane, Luggage and Cargo, Using Artificial Intelligence Method-Backpropagation Neural Network at Juanda International Airport," Warta Ardhia Jurnal Perhubungan Udara , vol. 45, no. 2, pp. 99-110, 2019.
L. S. Moonlight, "Sistem Pengenalan Wajah Berbasis Jaringan Syaraf Tiruan Self Organizing Map (SOM) Dengan Pemrosesan Awal Discrete Cosine Transform (DCT)," Jurnal Penelitian Politeknik Penerbangan Surabaya, vol. IV, no. 3, pp. 29-39, 2019.
H. Mohamed, "DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking," The Department of Electrical and Computer Engineering, Concordia University, Canada, 2016.
Ö. Aydogdu and M. Ekinci, "An Approach for Streaming Data Feature Extraction Based on Discrete Cosine Transform and Particle Swarm Optimization," Symmetry, vol. 12, no. 299, 2020.
I. G. N. M. K. Raya, A. N. Jati and R. E. Saputra, "Analysis realization of Viola-Jones method for face detection on CCTV camera based on embedded system," in International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics), Bali, Indonesia, 2017.
Ibrahim, "Analisis Akurasi Pengenalan Wajah menggunakan Algoritma Viola Jones dan Modified Self Organizing Map," Prodi S2 Teknik Informatika Universitas Sumatera Utara, Medan, 2020.
L. Tan, C. Li, J. Xia and J. Cao, "Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection," Computers, Materials & Continua (CMC), vol. 61, no. 1, pp. 275-288, 2019.
S. Aly and S. Almotairi, "Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition," IEEE Access, vol. 8, pp. 10735-10745, 2020.
S. Rawat, "Face Recognition Using Back Propagation Neural Network," International Journal of Innovations in Engineering and Technology (IJIET), vol. X, no. 4, pp. 125-132, 2018.
Yuhandri, E. Prasetyo, S. Madenda and Karmilasari , "Pattern Recognition and Classification using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval," International Journal on Advanced Science Engineering Information Technology, vol. 7, no. 6, pp. 2343-2349, 2017.
A. N. Gomez, M. Ren, R. Urtasun and R. B. Grosse, "The Reversible Residual Network:Backpropagation Without Storing Activations," in 31st Conference on Neural Information Processing Systems (NIPS), CA, USA, 2017.
J. Brownlee, "How to Code a Neural Network with Backpropagation In Python (from scratch)," 7 November 2017. [Online]. Available: https://machinelearningmastery.com/. [Accessed 26 August 2021].
Recogtech, "FAR and FRR: Security Level Versus User Convenience," Recogtech, 6 December 2017. [Online]. Available: https://www.recogtech.com/en/knowledge-base/security-level-versus-user-convenience. [Accessed 20 September 2021].
A. A. Syukur, B. Pramadi and Y. Abdurrozaq, "Implementasi Webcam sebagai Pendeteksi Wajah pada Sistem Keamanan Perumahan Menggunakan Image Processing," ELECTRICES, vol. 2, no. 1, pp. 1-5, 2020.
S. Shaik and C. Konda, "Performance of Biometrics : False Acceptance Rate ( FAR ) False Rejection Rate ( FRR ) Image Classification Techniques : SVM Classifier K-Nearest Neighbour Classifier Naive Bayes Decision Trees," International Journal of Scientific Development and Research (IJSDR), vol. 2, no. 7, pp. 287-292, 2017.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
