A Novel Discriminative Weighted Pooling Feature for Multi-view Face Detection.

Finding discriminative feature is crucial for building a high-performance object detection system, which has an effect on the detection speed and accuracy. In this paper, we propose a novel discriminative weighted pooling feature based on the multiple channel maps for multi-view face detection. The color and shape statistics of face structure can be utilized to enhance the discriminative ability of the box filter, which is generalized from the square channel filter. The discriminative information can be obtained with LDA and imbalance embedding LDA method, which is superior to the baseline box filter. The experimental result on the FDDB dataset shows that our proposed method has some advantages in accuracy or speed when compared with many other state-of-the-art methods.