Abstract
In cases of child pornography and child sexual abuse, criminals are usually careful to hide or cover their faces and tattoos, thus making identification difficult. However, naturally occurring skin marks can be observed in close-up views of their back, chest, or thighs, which are usually present in evidence images. Recently, a group of skin marks named Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) was proposed as a biometric trait for identification. Manual RPPVSM identification can be tiring and time consuming. We propose in this paper an automated RPPVSM identification system, which is composed of RPPVSM detection and matching algorithms. Three learning-based detection algorithms were developed to automatically detect RPPVSMs in color images. To evaluate these algorithms, experiments were performed on a database containing 216 back torso images from 118 subjects. The results show that high identification accuracy can be achieved and that the proposed RPPVSM identification system has high potential for forensic investigation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.