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With many practical applications in human life, including manufacturingsurveillance cameras, analyzing and processing customer behavior, manyresearchers are noticing face detection and head pose estimation on digitalimages. A large number of proposed deep learning models have state-of-the-artaccuracy such as YOLO, SSD, MTCNN, solving the problem of face detection orHopeNet, FSA-Net, RankPose model used for head pose estimation problem.According to many state-of-the-art methods, the pipeline of this task consistsof two parts, from face detection to head pose estimation. These two steps arecompletely independent and do not share information. This makes the model clearin setup but does not leverage most of the featured resources extracted in eachmodel. In this paper, we proposed the Multitask-Net model with the motivationto leverage the features extracted from the face detection model, sharing themwith the head pose estimation branch to improve accuracy. Also, with thevariety of data, the Euler angle domain representing the face is large, ourmodel can predict with results in the 360 Euler angle domain. Applying themultitask learning method, the Multitask-Net model can simultaneously predictthe position and direction of the human head. To increase the ability topredict the head direction of the model, we change there presentation of thehuman face from the Euler angle to vectors of the Rotation matrix.

Simultaneous face detection and 360 degree headpose estimation