LPHD: A Large-Scale Head Pose Dataset for RGB Images

Abstract

Head pose estimation has attracted many research interest in recent years. With the advent of deep learning, it is possible to predict the head pose accurately from the RGB images without the help of facial landmarks or depth information. However, existing head pose datasets often lack large pose head images, which extremely limits the development of head pose estimation algorithms. In this paper, we build the largescale head pose dataset (LHPD) including more than 140,000 images with the diverse and accurate head poses. The LHPD dataset includes the head images recorded from different shooting angles between the camera and the human body for the first time, which greatly expands the range of head pose compared to previous datasets. Therefore, the range of head pose can cover +/-90° for each Euler angle. The accurate and reliable head pose annotation is labeled by the motion capture system and careful calibration procedures. We then propose a head pose estimation method through fine-tuning the ResNet on the LHPD dataset when using the Euclidean distance of quaternions as the loss function. The results show that our method achieves better performance than current state-of-theart algorithms.