关键词: 多目标回归, 人脸检测, 检测评价函数, 卷积神经网络

Abstract: Real-time multi-objective regression algorithm optimizes four location parameters respectively and separates four location variables. It makes localization less accurate and training hard to converge. Based on the problems, this paper proposes a real-time multi-object regression face detection algorithm, combining detection evaluation function Intersection over Union（IoU） as loss function. Based on multi-objective regression algorithm model proposed by Redmond etc., it adopts its real-time detection mechanism. Then IoU is introduced as loss function of location parameter. Four independent location parameters in real-time multi-object regression model are integrated into one unit and optimized, avoiding the defects of the base model. The algorithm is tested in face detection benchmark FDDB. The results indicate that this algorithm is superior to a mainstream traditional one in terms of face detection’s effectiveness, and it outperforms other classical deep learning methods in terms of detection speed. The algorithm achieves a balance between face detection’s effectiveness and detection speed. It provides some reference value to construct practical face applications.

Key words: multi-objective regression, face detection, Intersection over Union（IoU）, convolutional neural network