  Facial attribute analysis in the real world scenario is very challenging
mainly because of complex face variations. Existing works of analyzing face
attributes are mostly based on the cropped and aligned face images. However,
this result in the capability of attribute prediction heavily relies on the
preprocessing of face detector. To address this problem, we present a novel
jointly learned deep architecture for both facial attribute analysis and face
detection. Our framework can process the natural images in the wild and our
experiments on CelebA and LFWA datasets clearly show that the state-of-the-art
performance is obtained.
