Facial Landmark Detection with Tweaked Convolutional Neural Networks

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Facial landmark detection is crucial for many computer vision applications.
• Existing methods struggle with variations in facial pose and appearance.
• Convolutional Neural Networks (CNNs) are widely used but can be improved for this task.

Purpose of the Study:

• To analyze intermediate features in CNNs for facial landmark detection.
• To develop a novel CNN architecture specialized for pose and appearance variations.
• To address data scarcity for extreme facial poses through data augmentation.

Main Methods:

• Unsupervised partitioning of facial images based on pose and properties from CNN intermediate features.
• Development of a specialized CNN architecture (Tweaked CNN - TCNN).
• Implementation of data augmentation techniques for pose-specific training data.

Main Results:

• The analysis revealed that CNNs naturally group faces by pose and appearance.
• The TCNN architecture demonstrated superior performance in facial landmark detection.
• TCNN outperformed existing methods on AFW, ALFW, and 300W benchmarks.

Conclusions:

• The proposed TCNN architecture offers improved facial landmark detection accuracy.
• Specialization and data augmentation are effective strategies for handling pose and appearance variations.
• The findings contribute to more robust facial analysis systems.