  Generic face detection algorithms do not perform very well in the mobile
domain due to significant presence of occluded and partially visible faces. One
promising technique to handle the challenge of partial faces is to design face
detectors based on facial segments. In this paper two such face detectors
namely, SegFace and DeepSegFace, are proposed that detect the presence of a
face given arbitrary combinations of certain face segments. Both methods use
proposals from facial segments as input that are found using weak boosted
classifiers. SegFace is a shallow and fast algorithm using traditional
features, tailored for situations where real time constraints must be
satisfied. On the other hand, DeepSegFace is a more powerful algorithm based on
a deep convolutional neutral network (DCNN) architecture. DeepSegFace offers
certain advantages over other DCNN-based face detectors as it requires
relatively little amount of data to train by utilizing a novel data
augmentation scheme and is very robust to occlusion by design. Extensive
experiments show the superiority of the proposed methods, specially
DeepSegFace, over other state-of-the-art face detectors in terms of
precision-recall and ROC curve on two mobile face datasets.
