A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

Area of Science:

• Computer Vision
• Machine Learning
• Biometrics

Background:

• Facial analysis technologies like detection, landmark localization, and recognition have advanced significantly for
• in-the-wild
• conditions.
• Deformable face tracking "in-the-wild" remains less evaluated, with performance often assessed qualitatively.

Purpose of the Study:

• To conduct the first comprehensive evaluation of state-of-the-art deformable face tracking pipelines on the 300VW benchmark.
• To analyze different on-line deformable face tracking strategies.

Main Methods:

• Evaluation of generic face detection with generic facial landmark localization.
• Assessment of generic model-free tracking with generic facial landmark localization.
• Comparison of hybrid approaches integrating advanced face detection, model-free tracking, and facial landmark localization.

Main Results:

• The study systematically evaluates multiple architectures for on-line deformable face tracking.
• Comparative analysis highlights the performance differences between generic and hybrid tracking strategies.

Conclusions:

• The research provides a thorough benchmark-driven evaluation of deformable face tracking "in-the-wild".
• Findings identify critical areas and future research directions for improving facial tracking accuracy and robustness.