Orientation Cues-Aware Facial Relationship Representation for Head Pose Estimation via Transformer

Area of Science:

• Computer Vision
• Machine Learning
• Human-Computer Interaction

Background:

• Head pose estimation (HPE) is crucial for applications like human-machine interaction and autonomous driving.
• Existing HPE methods struggle with challenges such as occlusion, low illumination, and extreme head orientations.

Purpose of the Study:

• To develop a novel, relationship-driven method for robust head pose estimation.
• To address the limitations of current HPE techniques in real-world scenarios.

Main Methods:

• A Transformer-based architecture, TokenHPE, is proposed, utilizing orientation tokens to encode facial regions.
• The method incorporates insights from intra- and cross-orientation relationships within head images.
• A token-guided multi-loss function is designed to enhance the learning of regional similarities and relationships.

Main Results:

• TokenHPE achieves state-of-the-art performance on three challenging benchmark HPE datasets.
• The proposed method demonstrates effectiveness in handling occlusion, low illumination, and extreme head poses.
• Qualitative visualizations confirm the efficacy of the token-learning approach.

Conclusions:

• The developed relationship-driven method significantly advances head pose estimation capabilities.
• TokenHPE offers a robust solution for practical HPE applications facing real-world challenges.
• The novel token-learning methodology proves effective for accurate and reliable head pose prediction.