Statistical analysis of fingerprint minutiae based on a large dataset and accurate minutiae detection method

Since the late 20th century, wrongful convictions based on fingerprint evidence and judicial scrutiny have raised questions about the scientific validity of fingerprint evidence, necessitating research into the scientific foundations of fingerprint identification. This article employs state-of-the-art AI algorithms to achieve fingerprint pattern classification, pose estimation, and minutiae detection. Based on a large-scale dataset of 620,211 fingerprint images, various analytical methods are applied to explore the relationships between fingerprint patterns, minutiae, and fingers, and statistical analysis is conducted on the quantity and spatial distribution of six types of minutiae: ridge endings, bifurcations, spurs, independent ridges, lakes, and crossovers. Compared with previous research, the average accuracy of minutiae detection is improved from 97.22% to 99.45%. Results indicate that whorls are the most common pattern, associated with thumbs and ring fingers, while loops are associated with middle and little fingers. The overall distribution ranges (in percentages) of the six types of minutiae are: ridge endings [58.288, 58.875], bifurcations [37.874, 38.421], spurs [1.301, 1.314], independent ridges [1.246, 1.260], lakes [0.415, 0.419], and crossovers [0.291, 0.295]. Spatial distribution analysis reveals that independent ridges exhibit concentrated distribution in delta regions, while the other types of minutiae are primarily concentrated in the core regions. This article quantifies the evidential value of different minutiae by analyzing the relationships among patterns, minutiae, and fingers as well as the spatial distribution of minutiae, providing a scientific statistical foundation for establishing probabilistic fingerprint identification models and contributing to improving objectivity and scientific rigor in fingerprint identification.