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The aim of this entry is to describe and explain the main forensic uses of fingermarks and fingerprints. It defines the concepts and provides the nomenclature related to forensic dactyloscopy. It describes the structure of the papillary ridges, the organization of the information in three levels, and its use for the fingerprint classification and individualization processes. It focuses on the variability and the distinctiveness of the marks and the prints and the exploitation of these properties in the forensic context. It emphasizes the difference between the properties of the mark and the prints in relation with the individualization process. It describes the current practice for fingermark evidence evaluation and analyzes the limits of forensic evaluation based on deterministic conclusions. It discusses the admissibility of the fingerprint evidence and provides casework examples involving misidentifications. It introduces the results of statistical research based on empirical data, statistical modeling, and an evaluation framework aiming at the description of the strength of evidence. Finally, it puts in perspective the current practice and the results of research and addresses the question of future developments in the field.

This article describes how the fields of biometrics and forensic science can contribute and benefit from each other. The aim is to foster the development of new methods and tools improving the current forensic biometric applications and allowing for the creation of new ones. The article begins with a definition and a summary of the development in forensic biometrics. Then it describes the data and biometric modalities of interest in forensic science and the forensic applications embedding biometric technology. On this basis it describes the solutions and limitations of the current practice regarding the data, the technology and the inference models. Finally, it proposes research orientations for the improvement of the current forensic biometric applications and suggests some ideas for the development of some new forensic biometric applications.

Recently, it has been shown that performance of a face recognition system depends on the quality of both face images participating in the recognition process: the reference and the test image. In the context of forensic face recognition, this observation has two implications: a) the quality of the trace (extracted from CCTV footage) constrains the performance achievable using a particular face recognition system; b) the quality of the suspect reference set (to which the trace is matched against) can be judiciously chosen to approach optimal recognition performance under such a constraint. Motivated by these recent findings, we propose a framework for forensic face recognition that is based on calibrating the recognition performance for the quality of pairs of images. The application of this framework to several mock-up forensic cases, created entirely from the MultiPIE dataset, shows that optimal recognition performance, under such a constraint, can be achieved by matching the quality (pose, illumination, and, imaging device) of the reference set to that of the trace. This improvement in recognition performance helps reduce the rate of misleading interpretation of the evidence.