  In digital and multimedia forensics, identification of child sexual offenders
based on digital evidence images is highly challenging due to the fact that the
offender's face or other obvious characteristics such as tattoos are occluded,
covered, or not visible at all. Nevertheless, other naked body parts, e.g.,
chest are still visible. Some researchers proposed skin marks, skin texture,
vein or androgenic hair patterns for criminal and victim identification. There
are no available studies of nipple-areola complex (NAC) for offender
identification. In this paper, we present a study of offender identification
based on the NAC, and we present NTU-Nipple-v1 dataset, which contains 2732
images of 428 different male nipple-areolae. Popular deep learning and
hand-crafted recognition methods are evaluated on the provided dataset. The
results indicate that the NAC can be a useful characteristic for offender
identification.
