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.

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.

Recent technological advances have allowed for a proliferation of digital images that may be involved in crimes. Using these images as evidence in legal cases like child pornography and masked gunmen can be challenging because usually the faces of the suspects are not visible. To perform personal identification in these images, we propose a biometric trait composed of a group of skin marks including, but not limited to, nevi, lentigines, cherry hemangiomas, and seborrheic keratoses. Due to their biological characteristics, we have grouped these as "Relatively Permanent Pigmented or Vascular Skin Marks," abbreviated as RPPVSM. As statistical study of RPPVSM is essential before investigating their discriminative power, we present in this paper the fundamental statistics of RPPVSM. Back torso images were collected from 144 Caucasian, Asian, and Latino males, and a researcher trained in dermatology manually identified their RPPVSMs. The statistical results show that Caucasians tend to have more RPPVSMs than Asians and Latinos, and over 80 percent of middle to low density RPPVSM patterns are independently and uniformly distributed.