Convolutional Neural Network Design Using a Particle Swarm Optimization for Face Recognition

In this work, the combination of convolutional neural networks (CNNs) and particle swarm optimization techniques to automatically design CNN architectures is proposed. The proposed particle swarm optimization aims at finding CNN parameters: the number of fully connected layers with their number of neurons, the number of convolutional layers with the number and filters size, batch size, and the epochs number. This method is applied and tested with face recognition datasets. The particular databases used in this work are the ORL and Yale, where the principal goal of this work is to reduce the recognition error. Experiments are performed using different images for the training phase to observe how much information is needed to obtain a good recognition. The simulation results show a face recognition rate of 100% with 7 (ORL) and 4 (Yale) images for the training phase, respectively. The obtained results are contrasted with other works to observe the behavior of the proposed method.