Lightweight CNN-based head pose estimation using heatmaps and anthropometric facial measures
Anam Memon, Ali Asghar Manjotho, Qasim Ali Arain, Adel A. Sulaiman, Nasrullah Pirzada, Mana Saleh Al Reshan, Mohammad Alsulami, Asadullah Shaikh · ICT Express · 2025
Head pose estimation from a monocular image is crucial for applications in computer vision, AR/VR, and human–computer interaction. However, it remains challenging due to occlusions, lighting variations, and limited data. Landmark-based methods often suffer from localization errors, while landmark-free models tend to be complex and computationally expensive. To address these issues, we propose a lightweight, landmark-free CNN regressor guided by anthropometric facial measures. The model comprises two components: an Anthropometric Facial Measure Regressor (AFMR) that estimates a 4D vector of key facial segment lengths, and a CNN-based module that generates five uncertainty-based facial heatmaps. Evaluations on the BIWI and AFLW datasets show that our method outperforms state-of-the-art approaches, reducing localization error by 0.13° and 0.67°, respectively, while achieving faster convergence, lower parameter count, and real-time suitability.