Research on small.scale, multi.angle face detection methods in complex backgrounds
Huang Jie，Liu Fen
School of Electronic Engineering, Tianjin University of Technology and Education
Abstract： A face detection approach which is named GhostNet.MTCNN was proposed to enhance the precision of small sized face detection in complex backgrounds. On the backbone of MTCNN, this approach uses the lower computational Ghost bottleneck module which is in the GhostNet to replace the convolutional function, and discards the common convolution which occupies computer resources to configure the network′s feature extraction function. Through the process, a new module will be set up. The experimental results showed that the approach can effectively balance parameter quantity and precision. Across three validation sets categorized as Easy, Medium and Hard, compared to the original MTCNN, the proposed GhostNet-MTCNN achieves notable improvements in accuracy respectively 5.6%, 6.6% and 7.8%, while the parameter quantity only with a minimal increase of 0.62M. Furthermore, compared to MobileNetV3-MTCNN, GhostNet-MTCNN outperforms by enhancing accuracy by 1.6%, 0.8% and 0.5%, meanwhile a reduction in parameter quantity by 1.27M. The study can not only enhance the precision of the module to detect the small-sized and multi-angle faces in complex backgrounds but also can effectively balance parameter quantity and detection precision, which will make it a superior choice for edge deployment devices.
Key words : face detection; multi-task cascaded convolutional networks; lightweight network; edge devices
