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.

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.