Can Hierarchical Transformers Learn Facial Geometry?

Area of Science:

• Computer Vision
• Machine Learning
• Biometrics

Background:

• Understanding human facial geometry is crucial for identity and expression analysis.
• Automated systems require effective facial feature modeling for accessibility.
• Hierarchical architectures can capture multi-resolution representations vital for facial data.

Purpose of the Study:

• To propose a hierarchical transformer architecture for robust facial geometry representation.
• To demonstrate the versatility of this architecture across multiple facial analysis tasks.
• To address challenges in facial modeling such as pose, occlusion, and background variations.

Main Methods:

• Utilized a hierarchical transformer architecture for facial geometry modeling.
• Employed the transformer as a backbone for face anti-spoofing, facial expression representation, and deepfake detection.
• Conducted experiments to evaluate performance against common facial modeling issues and on supplemental tasks.

Main Results:

• The proposed hierarchical transformer effectively captures facial geometry, including fine-grained details and global attention.
• The architecture demonstrates robustness to variations in pose, occlusions, and background.
• The model achieved strong performance on face anti-spoofing, facial expression representation, and deepfake detection tasks.

Conclusions:

• Hierarchical transformer architectures offer a powerful method for robust facial geometry representation.
• This approach provides a versatile backbone for various facial analysis applications.
• The method effectively handles common challenges in facial modeling, paving the way for improved biometric systems.