Enhancing facial landmark detection with ControlNet-based data augmentation

Abstrak

Facial landmark detection plays a pivotal role in various computer vision applications, including face recognition, expression analysis, and augmented reality. However, existing approaches often struggle with accuracy due to the variations in lighting, poses, and occlusion. To address these challenges, this study explores the integration of ControlNet with Stable Diffusion to enhance facial landmark detection via data augmentation. ControlNet, an advanced extension of diffusion models, improves image generation by conditioning outputs on structured inputs such as landmark coordinates, enabling precise control over image attributes. By leveraging annotated landmark data from the 300W dataset, ControlNet synthesizes diverse facial images that supplement traditional training datasets. Experimental results demonstrate that ControlNet-based augmentation reduces the interocular normalized mean error (INME) in landmark detection from a baseline of 4.67 to a range of 4.63 to 4.74, with optimal parameter tuning yielding further accuracy gains. These findings highlight the potential of generative models in complementing discriminative approaches and improving robustness and precision in facial landmark detection. The proposed method offers a scalable solution for enhancing model generalization, particularly in applications requiring high-fidelity facial analysis. Future research can extend this framework to broader computer vision tasks that demand detailed feature localization and structured data augmentation.