DADL: Double Asymmetric Distribution Learning for head pose estimation in wisdom museum

Abstract

Head pose estimation plays a pivotal role in various applications, including augmented reality and human–computer interaction within intelligent museum environments. Head pose estimation conventionally relies on hard labels. However, acquiring the “ground truth” through subjective means introduces an element of uncertainty into the labels for head pose estimation. The introduction of soft labels offers a potential remedy for this uncertainty. However, existing head pose estimation methods based on soft labels neglect the asymmetry of head pose. After careful observation, two types of asymmetry have been identified in human head pose: within angle and between angle asymmetry. Taking these two characteristics into account, we have devised a Double Asymmetric Distribution Learning (DADL) network model for the precise estimation of head pose angles. This model employs distinct soft label distribution mechanisms to capture within-angle and between-angle nuances in head pose variations. Thereby enhancing the interpretability, generalization capability, and classification accuracy of head pose estimation models. Extensive experiments were conducted on various widely recognized benchmarks, including the AFLW2000 and BIWI datasets. The results substantiate substantial advantages of our model over conventional approaches.