Robust facial landmark detection by cross-order cross-semantic deep network

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Convolutional Neural Networks (CNNs) have advanced facial landmark detection.
• Existing CNN methods struggle with occlusions and pose variations due to limited feature learning.
• Accurate modeling of relationships between local facial details and semantic features is challenging.

Purpose of the Study:

• To develop a robust facial landmark detection method that overcomes limitations of current CNN approaches.
• To improve the accuracy and resilience of facial landmark detection under challenging conditions like occlusions and large pose variations.
• To enhance the learning of discriminative and fine semantic features by activating multiple correlated facial parts.

Main Methods:

• Proposing a Cross-Order Cross-Semantic Deep Network (CCDN).
• Introducing a Cross-Order Two-Squeeze Multi-Excitation (CTM) module for learning cross-order channel correlations and activating attention-specific parts.
• Designing a novel Cross-Order Cross-Semantic (COCS) regularizer to learn cross-order cross-semantic features.

Main Results:

• The CCDN effectively activates and learns fine, complementary cross-order cross-semantic features.
• The proposed method demonstrates improved accuracy in facial landmark detection under extremely challenging scenarios.
• Experimental results show the superiority of CCDN over existing state-of-the-art methods on benchmark datasets.

Conclusions:

• The CCDN significantly enhances facial landmark detection robustness and accuracy.
• The integration of CTM module and COCS regularizer is key to learning advanced semantic features.
• The proposed approach offers a promising solution for real-world facial analysis applications facing occlusions and pose variations.