a value of 1. Fingers with a non-whorl pattern, such as an arch or loop pattern, have a value of zero. The sum of the even finger value is then calculated and placed in the numerator of a fraction. The sum of the odd finger values is placed in the denominator. The value of 1 is added to each sum of the whorls with the maximum obtainable on either side of the fraction being 32. Thus, the primary classification is a fraction between 1/1 to 32/32, where 1/1 would indicate no whorl patterns and 32/32 would mean that all fingers had whorl patterns. Example of a Henry Classification: plus 34edg Key- Ridge count first loop Major- Value of the ridge counts or the tracings of fingers #1, #6 Primary- Summation of the value of: Whorl type patterns fingers (#2, #4, #6, #8, #10 for Numerator), (#1, #3, #5, #7, #9 for Denominator); Value of fingers as whorls: #1 & #2 (16), #3 & #4 (8), #5 & #6 (4), #7 & #8 (2), #9 & #10 (1); Plus 1 in both Numerator and Denominator. Secondary- Pattern types in fingers #2 and #7, (U) Ulna Loop, (R) Radial Loop, (W) Whorl, and (A) Arch Sub-Secondary- Value of ridge counts or tracing- fingers #2, #3, #4 in Numerator; #7, #8, #9 in Denominator. Final- is the ridge count of the loops or whorls in both little fingers expressed in numbers. The right little is used as the numerator and the left little as the denominator. If a loop appears in one finger and a whorl in the other, enumerate both their ridge counts by treating the whorl as an ulnar loop. Impact on current biometric systems The Henry Classification System has been a highly influential force in the formation of current IAFIS technology (Integrated Automated Fingerprint Identification System). When IAFIS solutions attempted to emulate the Henry process. Up until the mid-1990s, it was not unusual for a state or city to continue to maintain its physical file of Henry-sorted fingerprint cards just in case a disaster occurred in the IAFIS. As processing speeds, network throughput capacities, and system reliability increased, it was no longer necessary for automated fingerprint matching to mirror what had been the manual processes. IAFIS began to classify fingerprints according to the distance between the core and delta, minutiae locations, and pattern type, the latter being based on the