Automatically search an optimal face detector for a specific deployment environment

Abstract Face detection plays an important role in many artificial intelligence applications, such as identity recognition, facial expression recognition, and gender/age recognition. Recently, the development of deep learning techniques has greatly improved face detection’s performance. However, it is still ineffective and time-consuming to manually design hyperparameters of face detectors for different deployment environments with diverse distributions. Besides, due to the limited computation capability, many previous networks are hard to meet the latency requirements in deployment environments, and the improved resolution of current cameras further increases the computation burden. Motivated by the above problems, we propose a searching framework aiming to automatically search a real-time face detector architecture with a fixed complexity constraint, to adapt a specific deployment environment. We model the whole searching space into two parts, including the hyperparameters of the network and the detector. Instead of only searching the network structure, the proposed method considers the whole model’s hyperparameters space which contains the preprocessing and postprocessing parameters. The evolutionary algorithm is employed to find the optimal solution, and new evolutionary operations are proposed to explore architecture space. During the whole searching procedure, we guarantee the computation cost is under the restrictions. The advantages of the proposed framework are that it considers a hard computation cost constraint and the preprocessing and postprocessing hyperparameters, leading to a fully automatic design style and global optimization. Finally, we evaluate the proposed model on the most popular Widerface and FDDB datasets. The proposed detector significantly surpasses the existing lightweight face detectors in the comprehensive performances, and the average latency is twice as shorter as the best competitor.