Abstract
Face recognition is one of the cornerstones of the face processing schemes that composed the contemporary intelligent vision-based interactive systems between computers and humans. Instead of using neurons of the Self-Organized Map (SOM) neural network to cluster the facial data, in this work, we applied an agglomerative hierarchical clustering to cluster the neurons of the SOM network, which in turns, used to cluster the facial dataset. In prior, Principal Component Analysis (PCA) is employed to reduce the dimension of the facial data as well as to establish the initial state of SOM neurons. The design of the clustered-SOM recognition engine involves post-training steps that labeled the clustered SOM neurons resulting in a supervised SOM network. The effectiveness of the proposed model is demonstrated using the well-known ORL database. Using five images per person for SOM training, the proposed recognizer results in a recognition rate of 94.7%, whereas using nine images raise the recognition rate up to 99.33%. The facial recognizer has attained a notable reliability and robustness against the additive white Gaussian noise, where increasing the level of noise variance from 0 to 0.09, the recognition rate decreased only by 8%. Furthermore, time cost is analyzed, where using 200 images for training takes less than 4 seconds to be performed, whereas testing using a new set of 200 images takes less than 0.013 seconds which is competitive to many artificial intelligence and machine learning based schemes.