Rotation Face Detection Based on Three-Window Convolutional Neural Networks

Abstract

In some family photos or special scene pictures, we can find some rotated faces. Most existing methods are based on increasing the features of rotated faces or changing the directions of pictures to augment the training data. However, these methods have their own limitations and can not detect rotated faces accurately. We propose a method based on three-window convolutional neural networks designed by ourselves. We extract the features of faces and change the feature matrices of faces by clockwise rotation and anticlockwise rotation through three-window convolutional layer in order to increase the face features. We retain parameters of the model after training, and convolutional layer replace fully connected layer. According to the heatmap of the sample, our method can predict face region. We carry out the experiments on FDDB and LFW datasets. The experiments on LFW show that our model achieves AUC of 0.9240, and recall of 0.9367 and the experiments on FDDB show that our model achieves recall of 0.9541.