RHPENet: Robust Head Pose Estimation by Learning Heterogeneous Relationships from Facial Region Cues in Human Robot Interaction

Head pose estimation (HPE) techniques frequently encounter difficulties when handling extreme angles, occlusions, and uneven lighting conditions. In this paper, we present a novel heterogeneous relationship learning framework designed to mitigate these limitations by exploiting facial regions of interest (FRoI) and their complex interdependencies. Our approach stems from two fundamental discoveries: first, the critical importance of FRoI for pose determination, and second, the heterogeneous relationship between neighboring postures. The proposed architecture consists of three main modules: region feature generator (RFG), hierarchical structural representation (HSR), and cross-relation aggregator (CRA). The RFG incorporates an adaptive attention mechanism that prioritizes diagnostically significant facial zones. Within the HSR component, we implement a novel "Rugby-style" cross-level connectivity pattern to enhance feature integration. The CRA employs Transformer-based techniques to uncover both spatial and angular dependencies. Comprehensive evaluations conducted on major HPE benchmarks (300W_LP, AFLW2000, and BIWI) demonstrate that our RHPENet model consistently outperforms existing approaches.