Fingerprints analysis using AI Algorithm

The gold standard for identifying someone in the forensic field for more than a century has been fingerprints. In the discipline of forensic science, fingerprints serve as the key piece of evidence for personal identification. A trusted method of identifying people, particularly in forensic investigations, is the study of fingerprints, also known as dactylography or dermatoglyphics. The foundation of the fingerprint system is the idea that the skin on the thumbs and balls of the fingers is covered with ridges and grooves, and that each person's unique pattern allows for absolute identification. Fingerprint identification is the ideal route to alleviate suspicions about the identity of the fingerprints when comparing them with the Automated Fingerprint Identification System (AFIS). Investigators retrieve three different types of prints at the crime scene: the patent fingerprint, the latent fingerprint, sometimes known as incomplete fingerprints, and the plastic fingerprint. Recognizing the fragmented fingerprints is a difficulty for fingerprint experts. This paper proposes a novel algorithm which compare the minutiae named or key-points of incomplete fingerprints and then compare it with rolled prints to give good matches and accuracy. Through this algorithmic technique, the fingerprint image is studied properly for the better identification. The method was made simple and dynamic by algorithms. This technique assists investigators in concluding a range of difficult cases with trustworthy results by comparing damaged and overlapped samples with the rolled prints. This aids detectives and professionals in fingerprinting in accurately solving a variety of challenging cases.