2 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
Why Temporal Persistence of Biometric Features is so Valuable for Classification Performance
Lee Friedman, Hal Stern, Larry R. Price +1
It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although t…
The Linear Relationship between Temporal Persistence, Number of Independent Features and Target EER
Lee Friedman, Hal S. Stern, Oleg V. Komogortsev
If you have a target level of biometric performance (e.g. EER = 5% or 0.1%), how many units of unique information (uncorrelated features) are needed to achieve that target? We show…
Biometric Performance as a Function of Gallery Size
Lee Friedman, Hal S Stern, Vladyslav Prokopenko +3
Many developers of biometric systems start with modest samples before general deployment. They are interested in how their systems will work with much larger samples. We evaluated…