This is a follow on paper to which examined the impact of gender on a fingerprint recognition system. In that study, the authors used two different technologies (capacitance and optical) single finger sensors. In this study, the authors examined the differences in gender using images automatically segmented from a 10-print fingerprint sensor. Therefore, we analyze in detail the fingerprint ... [Show full abstract] locations and assess the performance, image quality and Henry Classification. Our results concur with recent literature which shows no significant difference in Henry Classification across gender, although there is a significant difference across the different fingerprint locations. We do show that there is a difference in image quality (females averaging 81.929 and males averaging 84.196), with a resulting difference in performance. The female dataset performed at an Equal Error Rate of 0.42%, and the male dataset performing at an EER of 0.68%).