Fingerprint quality measurement should be calculated from a region of interest (ROI) rather than an entire fingerprint image. The majority orientation information from the ROI can be used to represent an image’s overall quality. Reducing the ROI size by two is based on a 500-dpi image scanning resolution. The ideal fingerprint image has ridges and valleys that are clear and computer readable. These ridges and valleys are what make each fingerprint image unique. When a fingerprint image has good quality, increased performance of automatic fingerprint recognition systems during the matching and identification process is able to be achieved. However, the operational environment, such as lighting or deteriorating equipment in which a fingerprint image was collected often causes poor image quality. An algorithm is needed which provides calculated quality scores quickly from collected images to assist them in rendering, accepting, and rejecting decisions. This paper presents a human-visual-perception-model-based algorithm for determining fingerprint image quality that results in a single score to represent the image’s quality level. Figures and references