Master Thesis - Department of Computer Science

are expressed as,
Gi = b GD − area(GDi),
Li = area(IDi). (3.4)
where area(Y ) is a function which returns area under curve Y. The lower
values of Gi and Li reduce the chance of false rejection and false acceptance,
respectively.
• Second Criterion (C2): Second criterion utilizes the deviation of mean and
variance of genuine and impostor distributions from their ideal distributions.
Ideally, genuine distribution should have mean as one (1) and variance as zero
(0). Similarly, for impostor distribution mean and variance should be zero
ideally. Here we have considered additive effect of the deviations of mean and
variance from ideal values. So goatishness and lambishness measure for i th
subject is written as,
Gi = (1 − µ(GDi)) + σ(GDi),
Li = µ(IDi) + σ(IDi). (3.5)
where µ and σ provides the mean and variance of their argument, respectively.
• Third Criterion (C3): Multiplicative effect of the deviations of mean and vari-
ance from their ideal values is considered as the third criterion. Here, the
goatishness and lambishness measures for i th subject are formulated as,
Gi = (1 − µ(GDi)) exp(σ(GDi)),
Li = µ(IDi) exp(σ(IDi)). (3.6)
• Fourth Criterion (C4): Fourth criterion uses the concept of performance metrics
used in biometry. ZeroFRR is the lowest FAR for which no false rejection
occurs. The threshold (t) corresponding to ZeroFRR, denoted by TZeroF RR, is
the minimum score among the genuine scores, as FRR becomes non-zero if the
operating threshold is set as greater than TZeroF RR. Let ID TZeroF RR
i
of impostor scores having values above TZeroF RR and can be defined as,
be the set
ID TZeroF RR
i = {x ∈ IDi | x > TZeroF RR}. (3.7)
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