  We present a face detection algorithm based on Deformable Part Models and
deep pyramidal features. The proposed method called DP2MFD is able to detect
faces of various sizes and poses in unconstrained conditions. It reduces the
gap in training and testing of DPM on deep features by adding a normalization
layer to the deep convolutional neural network (CNN). Extensive experiments on
four publicly available unconstrained face detection datasets show that our
method is able to capture the meaningful structure of faces and performs
significantly better than many competitive face detection algorithms.
