Head pose estimation, which computes the intrinsic Euler angles (yaw, pitch, roll) from a target human head, is crucial for gaze estimation, face alignment and 3D reconstruction. Traditional approaches to head pose estimation heavily relies on the accuracy of facial landmarks, and solve the correspondence problem between 2D facial landmarks and a mean 3D head model (ad-hoc fitting procedures), which seriously limited their performance, especially when the visibility of face is not in good condition. But existed landmark-free methods either treat head pose estimation as a sub-problem, or bring extra error during problem reduction. Therefore, in this paper, we present our efficient hybrid coarse-fine classification to deal with issues above. First of all, we extend previous work with stricter fine classification by increasing class number. Then, we introduce our hybrid coarse-fine classification scheme into the network. Integrate regression is adopted to get the final prediction. Our proposed approach to head pose estimation is evaluated on three challenging benchmarks, we achieve the state-of-the-art on AFLW2000 and BIWI, and our approach closes the gap with state-of-the-art on AFLW.
Another recent work done by @cite_16 achieves great performance on public datasets. They propose to use a higher level representation to regress the head pose while using deep learning architectures. They use the uncertainty maps in the form of 2D soft localization heatmap images over selected 5 facial landmarks, and pass them through an convolutional neural network as input channels to regress the head pose. However, this approach still cannot avoid problem of landmark invisibility even though they use coarse location, especially when considering that their method only involves with five landmarks which make their method very fragile to invisible condition.
@cite_16
2902228397
Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face. Annotating ground truth head pose angles for images in the wild is difficult and requires ad-hoc fitting procedures (which provides only coarse and approximate annotations). This highlights the need for approaches which can train on data captured in controlled environment and generalize on the images in the wild (with varying appearance and illumination of the face). Most present day deep learning approaches which learn a regression function directly on the input images fail to do so. To this end, we propose to use a higher level representation to regress the head pose while using deep learning architectures. More specifically, we use the uncertainty maps in the form of 2D soft localization heatmap images over five facial keypoints, namely left ear, right ear, left eye, right eye and nose, and pass them through an convolutional neural network to regress the head-pose. We show head pose estimation results on two challenging benchmarks BIWI and AFLW and our approach surpasses the state of the art on both the datasets.