An Approach for Accidental Identification Victim Using Fingerprint and Iris Technology

Dynamically identifying of accidental victims on roads are the crucial challenges faced internationally by Traffic authorities, hospitals, and police personal. This paper proposes an advanced system for automating the identification of accident victims, utilizing fingerprint and iris biometrics to retrieve personal data from official databases such as Aadhaar, PAN, and voter ID. Rapid identification is critical in emergencies that assist in notifying to family members that aids to facilitate for immediate medical care, and at the same time streamline administrative response from health insurance and police officials. Traditional methods, however, are often slow and unreliable, particularly when victims’ fingerprints or irises are damaged. To address these limitations, this system incorporates both fingerprint and iris recognition, along with a secondary identification method that enables data retrieval even when biometric data is compromised. In such cases, the system accepts alternative unique identifiers, including Aadhaar numbers, driver’s license, passport IDs, or PAN IDs, to access the victim’s information. The system leverages the Scale-Invariant Feature Transform (SIFT) algorithm to ensure high accuracy of 97.3%, overcoming the challenges of degraded fingerprints or other environmental impacts. By automating data retrieval and generating detailed reports, the system allows for the prompt notification of victims' families and reduces the workload on government and healthcare agencies. This approach not only improves the efficiency and accuracy of victim identification, but also ensures a more compassionate response, minimizing delays and distress for affected families. Consequently, this solution contributes significantly in enhancing the emergency response system by providing a reliable, multi-modal method for identifying accident victims under challenging circumstances.