Supervision-by-Hallucination-and-Transfer: A Weakly-Supervised Approach for Robust and Precise Facial Landmark Detection

2026-01-19

AI Summary

This work addresses the significant challenges posed by low resolution, data compression, insufficient training samples, and severe annotation noise in facial landmark detection. To overcome these limitations, the authors propose SHT, the first weakly supervised framework that jointly leverages face hallucination and pose transfer. The approach integrates a Dual Hallucination Learning Network (DHLN) and a Facial Pose Transfer Network (FPTN), which are co-optimized to mutually enhance both tasks. Without requiring fine-grained annotations, SHT substantially improves the robustness and accuracy of landmark detection on low-resolution images. Experimental results demonstrate that the method outperforms current state-of-the-art approaches in both face hallucination and facial landmark localization.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

High-precision facial landmark detection (FLD) relies on high-resolution deep feature representations. However, low-resolution face images or the compression (via pooling or strided convolution) of originally high-resolution images hinder the learning of such features, thereby reducing FLD accuracy. Moreover, insufficient training data and imprecise annotations further degrade performance. To address these challenges, we propose a weakly-supervised framework called Supervision-by-Hallucination-and-Transfer (SHT) for more robust and precise FLD. SHT contains two novel mutually enhanced modules: Dual Hallucination Learning Network (DHLN) and Facial Pose Transfer Network (FPTN). By incorporating FLD and face hallucination tasks, DHLN is able to learn high-resolution representations with low-resolution inputs for recovering both facial structures and local details and generating more effective landmark heatmaps. Then, by transforming faces from one pose to another, FPTN can further improve landmark heatmaps and faces hallucinated by DHLN for detecting more accurate landmarks. To the best of our knowledge, this is the first study to explore weakly-supervised FLD by integrating face hallucination and facial pose transfer tasks. Experimental results of both face hallucination and FLD demonstrate that our method surpasses state-of-the-art techniques.

Problem

• facial landmark detection
• low-resolution images
• weak supervision
• insufficient training data
• imprecise annotations

Innovation

• weakly-supervised learning
• facial landmark detection
• face hallucination
• pose transfer
• high-resolution feature representation