Quality-adaptive forensic face recognition using a dual-detector pipeline with AdaFace-ViT for postmortem identification

Identifying deceased individuals in forensic and humanitarian contexts, particularly those affected by torture and trauma, remains a significant challenge due to severe facial damage, decomposition, and substantial image disparities between antemortem (AM) and postmortem (PM) records. To address this issue, a novel automated framework for robust postmortem face recognition is proposed in this paper. The proposed methodology integrates a dual-method face detection strategy with automatic orientation correction to achieve a 95% detection rate on challenging PM images, drastically reducing manual intervention. For recognition, we leverage the quality-adaptive capabilities of AdaFace-ViT to generate discriminative embeddings. Extensive evaluation on a difficult dataset of AM and PM facial images shows the outstanding performance of the proposed framework. AdaFace-ViT outperformed FaceNet by 14.2% in top-5 accuracy and was 10 \(×\) more accurate with 3 \(×\) lower EER than traditional CNNs. The obtained results demonstrate the robustness of the proposed framework to extreme facial deterioration and its potential to enable scalable, reliable humanitarian identification systems.