Face Detection Method Based on Cascaded Convolutional Networks

Deep learning achieves substantial improvements in face detection. However, the existing methods need to input fixed-size images for image processing and most methods use a single network for feature extraction, which makes the model generalization ability weak. In response to the above problems, our framework leverages a cascaded architecture with three stages of deep convolutional networks to improve detection performance. The network can predict face in a coarse-to-fine manner. We replace the standard convolution with a combination of separable convolution and residual structure in the network. Extensive experiments on the challenging FDDB and WIDER FACE benchmarks demonstrate that our method achieves competitive accuracy to the state-of-the-art techniques while keeps real-time performance.