Optimizing Convolutional Neural Network Using Particle Swarm Optimization For Face Recognition

Abstract

Deep Convolutional neural networks are the emanating deep learning models outperforming various classic machine learning algorithms in resolving many disputes. Any deep convolutional neural networks (CNN) has hyper-parameters such as number of convolutional layer, number of filters, filter size, activation function, learning rate and number of fully connected layer. Generally, these hyper-parameters are selected manually and it varies for different models and datasets. The time consumption and computational resources are large when the structure of the neural network is complex. To avoid this, in this paper the CNN is optimized using particle swarm optimization (PSO) algorithm which converges faster than any other evolutionary algorithm and finds a better architecture for face recognition. The performance of PSO optimized CNN surpasses other algorithms. As well as the time consumption gets reduced.