  State-of-the-art methods of attribute detection from faces almost always
assume the presence of a full, unoccluded face. Hence, their performance
degrades for partially visible and occluded faces. In this paper, we introduce
SPLITFACE, a deep convolutional neural network-based method that is explicitly
designed to perform attribute detection in partially occluded faces. Taking
several facial segments and the full face as input, the proposed method takes a
data driven approach to determine which attributes are localized in which
facial segments. The unique architecture of the network allows each attribute
to be predicted by multiple segments, which permits the implementation of
committee machine techniques for combining local and global decisions to boost
performance. With access to segment-based predictions, SPLITFACE can predict
well those attributes which are localized in the visible parts of the face,
without having to rely on the presence of the whole face. We use the CelebA and
LFWA facial attribute datasets for standard evaluations. We also modify both
datasets, to occlude the faces, so that we can evaluate the performance of
attribute detection algorithms on partial faces. Our evaluation shows that
SPLITFACE significantly outperforms other recent methods especially for partial
faces.
