Subclass Contrastive Loss for Injured Face Recognition
This addresses the critical need for identifying victims in accidents or disasters to reunite families and aid investigations, representing a domain-specific incremental advance.
The paper tackles the problem of recognizing faces with injuries such as swelling or bruises, which disrupt standard recognition features, by proposing a Subclass Contrastive Loss (SCL) that surpasses existing algorithms for this task.
Deaths and injuries are common in road accidents, violence, and natural disaster. In such cases, one of the main tasks of responders is to retrieve the identity of the victims to reunite families and ensure proper identification of deceased/ injured individuals. Apart from this, identification of unidentified dead bodies due to violence and accidents is crucial for the police investigation. In the absence of identification cards, current practices for this task include DNA profiling and dental profiling. Face is one of the most commonly used and widely accepted biometric modalities for recognition. However, face recognition is challenging in the presence of facial injuries such as swelling, bruises, blood clots, laceration, and avulsion which affect the features used in recognition. In this paper, for the first time, we address the problem of injured face recognition and propose a novel Subclass Contrastive Loss (SCL) for this task. A novel database, termed as Injured Face (IF) database, is also created to instigate research in this direction. Experimental analysis shows that the proposed loss function surpasses existing algorithm for injured face recognition.