GMDL: Toward precise head pose estimation via Gaussian mixed distribution learning for students’ attention understanding

• A Gaussian mixed distribution label-based model is proposed for students’ head pose estimation. • The model can learn the discriminative facial features in each angle images. • Experimental results show that the GMDL model outperforms other existing models. Students’ head pose estimation is a very difficult task since the training data is insufficient for many head pose angles. In this study, we consider each head pose image as a Gaussian mixed distribution other than the traditional hard label, which the adjacent head pose images can provide supplementary information for the target image. Specifically, the Gaussian mixed distribution covers the current head pose image and its adjacent 24 head pose images. Each label of head pose image describes the similar degree between the current image and its adjacent head pose images. Then, a novel network architecture is proposed by constructing the Gaussian mixed distribution which learns more discriminative facial features. The extensive evaluations on two public HPE databases show that the proposed GMDL model obtains the better performance compared with the conventional algorithms. In practice, the proposed model can be utilized to estimate learners’ head pose angle for attention understanding in the instruction and learning scenarios.