<p>Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The implementation of the developed BP-ANN has been achieved using MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognition 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82%.</p>

Face recognition has become an interesting field for researchers where it is used in many applications. One of the most common methods of soft computing is named the artificial neural network (ANN) has been suggested to achieve the face recognition process. Nonetheless, the performance of ANN depends on the number of neurons in the hidden layers and the value of the learning rate. These variables are usually defined based on the trial and error method which is time-consuming. Furthermore, in many cases, it is very difficult to find the optimum value for these variables. Hence, to improve the performance of the ANN for the face recognition process, the optimization algorithm is needed to get promising outcomes. Therefore, this paper introduces an improved ANN design for face recognition using a meta-heuristic optimization algorithm. The ANN represents a distributed processing system consists of neurons which are simply connected elements. One of the most popular techniques for pattern recognition called back propagation algorithm (BP) is used to train the ANN (BP-ANN) to achieve the face recognition process. To enhance the face recognition system performance, the ANN has been hybridized with the well-known meta-heuristic optimization algorithm namely harmony search algorithm (HSA). The HSA based on the principle work of musicians to find the best harmonies. This technique is Implemented based on the results of the fitness function evaluation. In this research, the mean squared error (MSE) has been used as a fitness function. The HSA optimizes the ANN such that the face recognition system provides the lowest MSE and thus enhances the performance of the face recognition system. The accuracy of the optimum hybrid system (HSA-ANN) is investigated using the MATLAB environment conducted for 10 persons. The results revealed that the proposed system (HSA-ANN) achieved lower MSE compare with the ANN. Furthermore, the HSA-ANN gives a better face recognition rate than traditional ANN.

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