  Human face pose estimation aims at estimating the gazing direction or head
postures with 2D images. It gives some very important information such as
communicative gestures, saliency detection and so on, which attracts plenty of
attention recently. However, it is challenging because of complex background,
various orientations and face appearance visibility. Therefore, a descriptive
representation of face images and mapping it to poses are critical. In this
paper, we make use of multi-modal data and propose a novel face pose estimation
method that uses a novel deep learning framework named Multi-task Manifold Deep
Learning $M^2DL$. It is based on feature extraction with improved deep neural
networks and multi-modal mapping relationship with multi-task learning. In the
proposed deep learning based framework, Manifold Regularized Convolutional
Layers (MRCL) improve traditional convolutional layers by learning the
relationship among outputs of neurons. Besides, in the proposed mapping
relationship learning method, different modals of face representations are
naturally combined to learn the mapping function from face images to poses. In
this way, the computed mapping model with multiple tasks is improved.
Experimental results on three challenging benchmark datasets DPOSE, HPID and
BKHPD demonstrate the outstanding performance of $M^2DL$.
