Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99500
Title: The individuality of Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) in independently and uniformly distributed patterns
Authors: Nurhudatiana, Arfika
Kong, Adams Wai-Kin
Matinpour, Keyan
Chon, Deborah
Altieri, Lisa
Cho, Siu-Yeung
Craft, Noah
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2013
Source: Nurhudatiana, A., Kong, A. W.-K., Matinpour, K., Chon, D., Altieri, L., Cho, S.-Y., et al. (2013). The Individuality of Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) in Independently and Uniformly Distributed Patterns. IEEE Transactions on Information Forensics and Security, 8(6), 998-1012.
Series/Report no.: IEEE transactions on information forensics and security
Abstract: With recent advances in multimedia technology, the involvement of digital images/videos in crimes has been increasing significantly. Identification of individuals in these images/videos can be challenging. For example, in cases of child sexual abuse, child pornography, and masked gunmen, the faces of criminals or victims are often hidden or covered and only some body parts (e.g., back, thigh, and arm) can be observed from the digital evidence. Although tattoos and scars can be used for identification in some cases, they are neither universal nor unique. We propose a group of skin marks named Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) as a biometric trait for forensic identification. To support the scientific underpinnings of using RPPVSM patterns as a novel biometric trait, the individuality was studied. RPPVSM on the backs of 269 male subjects were examined. We found that RPPVSM in middle to low density patterns tend to form an independent and uniform distribution, while RPPVSM in high density patterns tend to form clusters. We present in this paper an individuality model for the independently and uniformly distributed RPPVSM patterns. When compared to the empirical results, this model fits the empirical distribution very well. Finally, the predicted error rates for verification and identification are reported.
URI: https://hdl.handle.net/10356/99500
http://hdl.handle.net/10220/17369
DOI: 10.1109/TIFS.2013.2258338
Schools: School of Computer Engineering 
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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