Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network

TL;DR

This paper introduces a novel deep network architecture that enhances facial landmark detection by activating multiple facial parts and learning diverse semantic features, improving robustness against occlusions and pose variations.

Contribution

The paper proposes the cross-order cross-semantic deep network (CCDN) with a new CTM module and COCS regularizer for better semantic feature learning in facial landmark detection.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively handles occlusions and pose variations.

Activates multiple facial parts for detailed feature extraction.

Abstract

Recently, convolutional neural networks (CNNs)-based facial landmark detection methods have achieved great success. However, most of existing CNN-based facial landmark detection methods have not attempted to activate multiple correlated facial parts and learn different semantic features from them that they can not accurately model the relationships among the local details and can not fully explore more discriminative and fine semantic features, thus they suffer from partial occlusions and large pose variations. To address these problems, we propose a cross-order cross-semantic deep network (CCDN) to boost the semantic features learning for robust facial landmark detection. Specifically, a cross-order two-squeeze multi-excitation (CTM) module is proposed to introduce the cross-order channel correlations for more discriminative representations learning and multiple attention-specific part activation. Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection. It is interesting to show that by integrating the CTM module and COCS regularizer, the proposed CCDN can effectively activate and learn more fine and complementary cross-order cross-semantic features to improve the accuracy of facial landmark detection under extremely challenging scenarios. Experimental results on challenging benchmark datasets demonstrate the superiority of our CCDN over state-of-the-art facial landmark detection methods.