Extra Facial Landmark Localization via Global Shape Reconstruction

Area of Science:

• Computer Vision
• Machine Learning
• Biometrics

Background:

• Facial landmark localization is crucial for face analysis.
• Current methods struggle with pose variations, occlusions, and model efficiency.
• Need for robust and efficient facial landmark detection.

Purpose of the Study:

• To present a global shape reconstruction method for locating extra facial landmarks.
• To improve efficiency and reduce model size in facial landmark detection.
• To address challenges of pose variations and occlusions.

Main Methods:

• Decomposition of reduced facial landmark configurations into sparse coefficients.
• Exploiting face shape correlations to regress between different landmark configurations.
• Reconstruction of extra facial landmarks using a pretrained shape dictionary and sparse coefficient approximation.

Main Results:

• Significant reduction in training time and model size for existing methods.
• Minor compromise in detection accuracy compared to traditional methods.
• Successful reconstruction of extra facial landmarks even with highly asymmetrical face poses.

Conclusions:

• The proposed global shape reconstruction method is feasible and effective.
• Enables more efficient and scalable facial landmark localization.
• Offers a robust solution for handling challenging facial poses and occlusions.