Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

Abstract

Real-world face recognition applications often deal with suboptimal image quality or resolution due to different capturing conditions such as various subject-to-camera distances, poor camera settings, or motion blur. This characteristic has an unignorable effect on performance. Recent cross-resolution face recognition approaches used simple, arbitrary, and unrealistic down- and up-scaling techniques to measure robustness against real-world edge-cases in image quality. Thus, we propose a new standardized benchmark dataset and evaluation protocol derived from the famous Labeled Faces in the Wild (LFW). In contrast to previous derivatives, which focus on pose, age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in the Wild (XQLFW) maximizes the quality difference. It contains only more realistic synthetically degraded images when necessary. Our proposed dataset is then used to further investigate the influence of image quality on several state-of-the-art approaches. With XQLFW, we show that these models perform differently in cross-quality cases, and hence, the generalizing capability is not accurately predicted by their performance on LFW. Additionally, we report baseline accuracy with recent deep learning models explicitly trained for cross-resolution applications and evaluate the susceptibility to image quality. To encourage further research in cross-resolution face recognition and incite the assessment of image quality robustness, we publish the database and code for evaluation.

arXiv comment9 pages, 4 figures, 2 tables

References in corpus (7)

• Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
• Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
• Masked Face Recognition Dataset and Application
• Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
• Blind Super-Resolution Kernel Estimation using an Internal-GAN
• Cross-Resolution Learning for Face Recognition
• Face Transformer for Recognition

Cited by in corpus (3)

• Explainable Model-Agnostic Similarity and Confidence in Face Verification
• Octuplet Loss: Make Face Recognition Robust to Image Resolution
• IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-Labels