Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition
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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
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