Henry Classification and Its Impact on Performance
The objective of this research was to perform an analysis of quality of the image, minutiae count, and overall performance of fingerprint images based on the Henry system of fingerprint classification and the finger’s relative location on the presenting hand. To do this, 50 users submitted 3 images from four fingers (index, middle, ring, and little). The National Institute of Standards and Technology (NIST) Fingerprint Image Software, release 2 (NFIS2) was used to analyze image quality and minutiae count. Neurotechnology Ltd.’s VeriFinger 4.2 was used to perform the matching operations to generate error rates and analyze performance. The results showed differences not only in image quality and minutiae count, but also matching performance based on Henry system classification and finger location.
Out of the five Henry classifications, the whorl performed above average, and was the highest ranked in image quality and minutiae. The research also indicted a correlation between matching performance with finger location. From a systems implementation standpoint, while not every user of a fingerprint system may possess a whorl classification using whorl classifications over the others will improve the performance of the system. At the very least, an index finger would be preferable over other fingers to achieve the best possible system performance. From a system development standpoint, knowledge about how different classifications and finger locations can be utilized to fine tune the system. Adjustments can be made in the form of a weighted algorithm to be more or less stringent during the matching process, based on the images being compared.