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Human Identification Using Automatic and Semi‐Automatically Detected Facial Marks
Author(s) -
Srinivas Nisha,
Flynn Patrick J.,
Vorder Bruegge Richard W.
Publication year - 2016
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12923
Subject(s) - computer science , biometrics , artificial intelligence , law enforcement , face (sociological concept) , identification (biology) , computer vision , crime scene , identity (music) , field (mathematics) , facial recognition system , pattern recognition (psychology) , psychology , law , social science , botany , physics , mathematics , criminology , sociology , political science , acoustics , pure mathematics , biology
Continuing advancements in the field of digital cameras and surveillance imaging devices have led law enforcement and intelligence agencies to use analysis of images and videos for the investigation and prosecution of crime. When determining identity from photographic evidence, forensic analysts perform comparison of visible facial features manually, which is inefficient. In this study, we will address research efforts to use facial marks as biometric signatures to distinguish between individuals. We propose two systems to assist forensic analysts during photographic comparison: an improved multiscale facial mark system in which facial marks are detected automatically, and a semi‐automatic facial mark system that integrates human knowledge within the improved multiscale facial mark system. Experiment results employ a high‐resolution time‐elapsed dataset acquired at the University of Notre Dame between 2009 and 2011. The results indicate that the geometric distributions of facial mark patterns can be used to distinguish between individuals.