Abstract

Tracing and recognizing the unique identity of the perpetrators of crime is a critical factor in detecting, apprehending, and eventually penalizing the culprits. There are numerous ways to detect the crime suspect, like onsite presence, availability of relevant documents, fingerprints, smart phone data, habitual trails, and other evidences procured from the crime scene. But, the most convincing evidence for identifying a culprit is the availability of the impressions of fingerprints accidentally left by the person on the objects in the crime scene. The partial finger impressions those are accidently left by criminals on different objects at the crime scene are referred as latent fingerprints. These latent fingerprints have to be analyzed using efficient research techniques, because a mistake in the analysis would mean the incarceration of an innocent person while the real culprit may walk free. So, researchers have developed numerous research concepts and techniques for the analysis of the feature components and the detection of latent fingerprints. Features are the essential components to determine the minutiae information of fingerprints. In this research work, the different techniques adapted by researchers for the detection of latent fingerprints are analyzed and discussed. This work, also compares the different techniques based on the datasets used for the research and feature analysis approach, and the latent fingerprints detection techniques.