On Criminal Identification in Color Skin Images Using Skin Marks (RPPVSM) and Fusion With Inferred Vein Patterns
School of Computer Engineering, Nanyang Technological University, Singapore
Arfika Nurhudatiana (S’11) received the B.E. (Hons.) degree in computer engineering and the Ph.D. degree from Nanyang Technological University, Singapore. Her research interests include image processing, pattern recognition, and biometrics.
Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) were recently introduced as a biometric trait for identification in the cases in which the evidence images show only the nonfacial body parts of the criminals or victims, such as in child sexual abuse and riots. As manual RPPVSM identification is tiring and time-consuming, an automated RPPVSM identification system is proposed in this paper. The system comprises skin segmentation, RPPVSM detection, and RPPVSM matching algorithms. The system was evaluated on 1,200 back images collected from 283 Asian and Caucasian subjects in varying pose and viewpoint conditions. The system achieved rank-1 and rank-10 identification accuracies of 76.79% and 88.97%, respectively, higher than the identification accuracies given by existing skin mark detection methods previously proposed for face recognition systems. To handle identification with limited numbers of RPPVSM, a fusion scheme with inferred vein patterns is also proposed. The fusion was evaluated on 2,360 images of chests, forearms, and thighs collected mostly from Asian subjects, who tend to have fewer RPPVSM than Caucasian subjects. The results show that the fusion improves vein identification in all body parts with improvement rates varying between 2% and 5% depending on the number of RPPVSM detected. To the best of our knowledge, this is the first work on automated identification in color skin images based on nonfacial skin marks and fusion with inferred vein patterns in forensic settings.
Published in: IEEE Transactions on Information Forensics and Security ( Volume: 10, Issue: 5, May 2015)
Page(s): 916 – 931
Date of Publication: 05 January 2015
INSPEC Accession Number: 15020279