Light-Weight Anchor Free Face Detection Based on Multi-Feature Fusion.

In view of the slow detection speed caused by the huge amount of computation and parameters in deep convolutional neural networks and the low accuracy by complex scenes in nowadays face detection, this paper presents an anchorfree face detection algorithm combing light-weight backbone and multi-feature fusion. The method firstly constructs a light-weight convolutional neural network as the backbone for feature extraction. Then three different modules are used to process the feature fusion:receptive field enhancement module to strengthen image information extraction, weighted feature fusion module to improve detection accuracy, channel shuffle fusion module to simplify the detection head. Finally, the center point anchor-free detection method is used to calculate the fused features and predict the position of faces. The experimental results show that the final model parameter is only 5.1 MB. The detection accuracy of easy, medium and hard in WIDERFACE verification set are improved by 1.4, 2.2 and 4.8 percentage points respectively compared with the benchmark method, which shows that the method has high accuracy in complex scenes face detection with a light-weight model and verifies the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]