Face Detection under Non-uniform Low Light Based on Improved MTCNN

Abstract

In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN's face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN's face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 %. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.