ARHPE: Asymmetric Relation-Aware Representation Learning for Head Pose Estimation in Industrial Human–Computer Interaction

Head pose estimation (HPE) has wide industrial applications, such as online education, human–robot interaction, and automatic manufacturing. In this article, we address two key problems in HPE based on label learning and asymmetric relation cues: 1) how to bridge the gap between the better prediction performance of networks and incorrectly label pose images in the HPE datasets and 2) how to take full advantage of the adjacent poses information around the centered pose image. We reconstruct all the incorrect labels as a two-dimensional Lorentz distribution to tackle the first problem. Instead of directly adopting the angle values as hard labels, we assign part of the probability values ( soft labels) to…

• NN
  National Natural Science Foundation of China

  Awards: 62005092, 61875068, 62177018, 62173286, 62077020, 62011530436