Robust Face Super-Resolution and Recognition Through Multi-Feature Aggregation in Diffusion Models

2026-07-06

AI Summary

This work addresses the challenge of face super-resolution in surveillance scenarios, where low resolution, pose variations, uneven illumination, and occlusions severely hinder recognition performance. Conventional super-resolution methods often introduce identity distortions that degrade downstream tasks. To overcome this, the authors propose FASR++, a novel approach that, for the first time, integrates features from a single reference low-resolution image and multiple auxiliary low-quality frames within a diffusion model framework. By leveraging a multi-feature aggregation mechanism, FASR++ achieves identity-preserving face super-resolution without relying on explicit soft attribute annotations or gradient-based guidance. The method effectively exploits redundant information across video sequences and establishes state-of-the-art performance on standard benchmarks, simultaneously advancing both reconstruction quality (as measured by PSNR, SSIM, and LPIPS) and face recognition accuracy (in verification and identification tasks).

Abstract

Images acquired in surveillance environments often suffer from conditions such as low resolution, variations in pose, irregular illumination, and occlusions. Due to the low quality of these images, face recognition algorithms often struggle. This major limitation can be addressed by employing super-resolution techniques that enhance the details of the image. However, due to the high degree of difficulty of the problem, most super-resolution algorithms tend to cause distortions in the image and in the individual's identity. Thus, additional information must be incorporated into the processing to improve recognition robustness. In this regard, surveillance cameras can capture multiple images, even at low quality, and the data extracted from these images, such as consecutive video frames, can significantly enhance both super-resolution and facial recognition. In this work, we introduce FASR++, a diffusion-model-based super-resolution algorithm. It leverages a reference low-resolution image and features extracted from multiple auxiliary low-quality images to generate a super-resolved output, minimizing distortions in the individual's identity. Our approach recovers facial features without explicitly providing soft attributes or computing a function gradient to guide the reconstruction process. FASR++ generates high-quality images that can considerably improve performance in face recognition tasks when used as a pre-processing step. We validate our approach on two standard face recognition datasets and attain state-of-the-art results for verification, face recognition, and image quality metrics such as PSNR, SSIM, and LPIPS.

Problem

• Face Super-Resolution
• Face Recognition
• Low-Quality Images
• Identity Preservation
• Surveillance Imaging

Innovation

• diffusion models
• multi-feature aggregation
• face super-resolution
• identity preservation
• low-quality face recognition