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 evidence images. Using these images as evidence in legal cases (e.g. child sexual abuse, child pornography and masked gunmen) can be very challenging, because the faces of criminals or victims are not visible. Although large skin marks and tattoos have been used, they are ineffective in some legal cases, because the skin exposed in evidence images neither have unique tattoos nor enough skin marks for identification. The blood vessel between the skin and the muscle covering most parts of the human body is a powerful biometric trait, because of its universality, permanence and distinctiveness. Traditionally, it was impossible to use vein patterns for forensic identification, because they were not visible in color images. This paper proposes an algorithm to uncover vein patterns from the skin exposed in color images for personal identification. Based on the principles of optics and skin biophysics, we modeled the inverse process of skin color formation in an image and derived spatial distributions of biophysical parameters from color images, where vein patterns can be observed. Experimental results are very encouraging. The clarity of the vein patterns in resultant images is comparable to or even better than that in near infrared images.

Identifying criminals and victims in evidence images, where their faces are covered or obstructed, is a challenging task. In the legal case, United States v. Michael Joseph Pepe (2008), Craft and Kong, who served as expert witnesses, used nevi to identify a pedophile in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affected the visibility of the nevi. In addition to this case, a huge amount of JPEG compressed child pornography is posted on-line every day. Although many methods have been proposed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based algorithm, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training dataset which contains original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer original blocks of compressed evidence images. An indexing mechanism is also proposed to deal with large datasets efficiently. Extensive experiments are conducted on images with different characteristics and compression ratios. Both visual comparison and subjective evaluation demonstrate that the proposed algorithm is more effective than other methods.

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.

Identifying individuals in evidence images, where their faces are covered or obstructed, is a challenging task. In the legal case, United States v. Michael Joseph Pepe (2008), Craft and Kong, who served as expert witnesses, used pigmented skin marks to identify a suspect in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affect the visibility of the small skin marks. In addition to this case, a huge amount of JPEG-compressed child pornography is posted online every day. Although many methods have been developed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based (KB) approach, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training set containing both original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer the original blocks of compressed evidence images. A Markov-model-based algorithm and a faster one-pass algorithm were developed to make inference, and a block synthesis algorithm was developed to handle the cases where compressed blocks are not contained in the training set. An indexing mechanism was also proposed to deal with large datasets efficiently. Extensive experiments were conducted on images with different characteristics and compression ratios. Both subjective and objective evaluations demonstrated that the KB approach is more effective than other methods. In summary, the KB approach is capable of removing blocking artifacts to recover useful skin features.