Face Detection with End to End Integration of a ConvNet and a 3D Model
This paper presents a method for face detection in the wild, which integrates a ConvNet and a 3D mean face model in an end to end multi task discriminative learning framework. The 3D mean face model is predefined and fixed (e.g. we used the one provided in the AFLW dataset) The ConvNet consists of two components: (i) The face proposal component computes face bounding box proposals via estimating facial key points and the 3D transformation (rotation and translation) parameters for each predicted key point w.r.t. the 3D mean face model. (ii) The face verification component computes detection results by pruning and refining proposals based on facial key points based configuration pooling. The proposed method addresses two issues in adapting state of the art generic object detection ConvNets (e.g. faster R CNN) for face detection: (i) One is to eliminate the heuristic design of predefined anchor boxes in the region proposals network (RPN) by exploiting a 3D mean face model. (ii) The other is to replace the generic RoI (Region of Interest) pooling layer with a configuration pooling layer to respect underlying object structures. The multi task loss consists of three terms: the classification Softmax loss and the location smooth (l_1\ losses of both the facial key points and the face bounding boxes. In experiments, our ConvNet is trained on the AFLW dataset only and tested on the FDDB benchmark with fine tuning and on the AFW benchmark without fine tuning. The proposed method obtains very competitive state of the art performance in the two benchmarks.
ECCV