Multi-Task Explainable Quality Networks for Large-Scale Forensic Facial Recognition

Identifying suspects from surveillance footage is a crucial task in forensic investigations, but it is often hindered by the variable conditions of observation and the large amounts of data. Face image quality (FIQ) is a metric that measures the usefulness of a face sample for facial recognition. Existing methods for automated FIQ assessment only provide a scalar value for quality, and do not indicate which factors are causing low quality. Additionally, these methods are computationally expensive, which makes current FIQ assessment methods unsuitable for large numbers of images. To address these issues, we introduce multi-task explainable quality networks (XQNets). XQNets provide both the quality value and the associated facial and environmental attributes, and automatically learn how each attribute contributes to the quality value during the training process. We also propose a dataset-agnostic quality pairing protocol to ensure that sample pairs are balanced across datasets and evaluations are fair. Our experiments on the LFW and SCface benchmarks show that our approach generalizes well across different datasets and outperforms state-of-the-art methods. Our method offers a fast, explainable approach to FIQ assessment, making it suitable for large-scale forensic applications.