Recognizing Injured Faces via SCIFI Loss

• Published in: IEEE transactions on biometrics, behavior, and identity science behavior, and identity science, 2021-01, Vol.3 (1), p.112-123
• Main Authors: Majumdar, Puspita, Chhabra, Saheb, Singh, Richa, Vatsa, Mayank
• Format: Article
• Language: English
• Subjects: Accidents; biometrics; Criminal investigations; Dentistry; DNA; face identification; Face recognition; Faces; Injured face recognition; Injuries; injury; Iris recognition; Natural disasters; Police; Violence
• ISSN: 2637-6407; 2637-6407
• DOI: 10.1109/TBIOM.2020.3047274

Deaths and injuries are common in road accidents, violence, and natural disaster. In accidents and natural disasters scenarios, one of the tasks of responders is to retrieve the identity of the victims to reunite families or ensure proper identification of deceased persons. Apart from this, the identification of unidentified dead bodies due to violence and accidents is crucial for the police investigation. In the absence of identification cards, different forensic techniques such as DNA profiling and dental profiling may be used for identification. In this research, we present face recognition as a fast and viable approach for recognizing individuals with injuries. Face, which can be captured easily, is one of the most commonly used and widely accepted biometric modalities. However, face recognition is challenging in the presence of injuries as facial injuries change the appearance and geometric properties of the face due to swelling, bruises, blood clots, and accidental cuts. These changes introduce large intra-class variations among the same subject and small inter-class separability among different subjects. To address the challenge, we propose a novel Subclass Injured Face Identification (SCIFI) loss which is used in learning feature representation agnostic to injury variations. Additionally, an extended Injured Face (IF-V2) database of 150 subjects is presented to evaluate the performance of face recognition models. Multiple experiments and comparisons are performed to showcase the efficacy of the proposed SCIFI loss based face recognition.