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Identifying criminals and victims in images (e.g., child pornography and masked gunmen) can be a challenging task, especially when neither their faces nor tattoos are observable. Skin mark patterns and blood vessel patterns are recently proposed to address this problem. However, they are invisible in low-resolution images and dense androgenic hair can cover them completely. Medical research results have implied that androgenic hair patterns are a stable biometric trait and have potential to overcome the weaknesses of skin mark patterns and blood vessel patterns. To the best of our knowledge, no one has studied androgenic hair patterns for criminal and victim identification before. This paper aims to study matching performance of androgenic hair patterns in low-resolution images. An algorithm designed for this paper uses Gabor filters to compute orientation fields of androgenic hair patterns, histograms on a dynamic grid system to describe their local orientation fields, and the blockwise Chi-square distance to measure the dissimilarity between two patterns. The 4552 images from 283 different legs with resolutions of 25, 18.75, 12.5, and 6.25 dpi were examined. The experimental results indicate that androgenic hair patterns even in low-resolution images are an effective biometric trait and the proposed Gabor orientation histograms are comparable with other well-known texture recognition methods, including local binary patterns, local Gabor binary patterns, and histograms of oriented gradients.