Improved MTCNN face detection algorithm fused with context features

Abstract

The multi-task convolutional neural network (MTCNN) face detection algorithm has a low detection rate of small faces in classroom scenes. An improved MTCNN algorithm that integrates context features was thus proposed. Firstly, the context convolution module was integrated with the R-Net layer network of the MTCNN model, and the feature map receptive field was expanded to obtain more small face information. Secondly, the deconvolution layer and the max-pooling layer were introduced to solve the problem of inconsistency of feature fusion data dimensions. Finally, the O-Net layer network of the MTCNN model integrated the context convolution module to further extract the small face feature information, and two convolutional pooling layers were introduced for feature fusion. The experimental results show the detection accuracy increased by 3% on the FDDB data set, the face detection recall rate and the F1 score increased by 8.69% and 4.94% respectively on the classroom scene data set compared with those of MTCNN algorithm.

Keywords

• context
• feature fusion
• multi-task convolutional neural network
• face detection
• classroom scenes
• small faces