Neural Architecture Search based on Brain Storm Optimization Algorithm for Face Detection

Deep learning has emerged in many practical applications, such as vascular segmentation, emotion recognition, and target detection. Moreover, convolutional neural networks (CNNs), representative techniques of deep learning, have been used to solve face recognition. However, the current design of CNNs for face recognition is highly dependent on domain knowledge and needs a large amount of trial and error. In this paper, we propose a new method based on the brain storm optimization algorithm to tackle the network structure and training parameters selection problem of CNN for face detection. In our method, an efficient mixed-length encoding strategy is designed to represent the CNN network information and the model training parameters. Specifically, convolutional layers, pooling layers, and fully connected layers can be designed automatically, and then an optimal set of selection results can be obtained. A series of experimental results show that the architecture by our method achieves higher accuracy (98.7%) compared to the state-of-the-art results on the FDDB dataset, as well as competitive results (accuracy = 94.3%, 92.5%, and 84.9%) on the WIDER FACE dataset.