Human Identification Using Automatic and Semi-Automatically Detected Facial Marks
| Autor: | Nisha Srinivas, Patrick J. Flynn, Richard W. Vorder Bruegge |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Biometry
Biometrics Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology Field (computer science) Pathology and Forensic Medicine Genetics Photography Humans Computer vision ComputingMethodologies_COMPUTERGRAPHICS 0505 law 050502 law 021110 strategic defence & security studies business.industry 05 social sciences Human knowledge Identification (information) Face Identity (object-oriented programming) Forensic Anthropology Artificial intelligence Crime Surveillance imaging business |
| Zdroj: | Journal of forensic sciences. 61 |
| ISSN: | 1556-4029 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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