An Improved Artificial Neural Network Design for Face Recognition utilizing Harmony Search Algorithm

Abstract

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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Research on Face Recognition Technology of Subway Automatic Ticketing System based on Neural Network and Deep Learning

“…BP algorithm can be used to train artificial neural network to realize face recognition process. To improve the performance of face recognition system, artificial neural network is combined with the famous meta heuristic optimization algorithm, namely harmony search algorithm (HSA) [12]. Genetic algorithm, particle swarm optimization algorithm, etc.…”

“…i in formula (1) represents the ith parameter, jth parameter is represented by j, and the samples' total number is represented by m. The subsampling layer can use a nonlinear downsampling method to reduce the amount of input data, thereby the feature map's scale is reduced without affecting the feature extraction of the target. If the size of uniform sampling is 12 SS  , then the convolution formula (2) can be obtained. As a CNN classifier, the fully connected layer can be implemented by convolution operations in practical applications, and is widely used in deep learning network models, but there is also the problem of parameter redundancy.…”