Multimodal fusion decision logic system for determining whether to accept a specimen
The present invention includes a method of deciding whether a data set is acceptable for making a decision. A first probability partition array and a second probability partition array may be provided. One or both of the probability partition arrays may be a Copula model. A no-match zone may be established and used to calculate a false-acceptance-rate (“FAR”) and/or a false-rejection-rate (“FRR”) for the data set. The FAR and/or the FAR may be compared to desired rates. Based on the comparison, the data set may be either accepted or rejected. The invention may also be embodied as a computer readable memory device for executing the methods.
1. A method of deciding whether to accept a specimen, comprising:
provide a data set having information pieces about objects, each object having a number of modalities, the number being at least two;
determine a first probability partition array (“Pm(i,j)”), the Pm(i,j) being comprised of probability values for information pieces in the data set, each probability value in the Pm(i,j) corresponding to the probability of an authentic match;
determine a second probability partition array (“Pfm(i,j)”), the Pfm(i,j) being comprised of probability values for information pieces in the data set, each probability value in the Pfm(i,j) corresponding to the probability of a false match;
identify a first index set (“A”), the indices in set A being the (i,j) indices that have values in both Pfm(i,j) and Pm(i,j);
identify a no-match zone (“Z∞”) that includes the indices of set A for which Pm(i,j) is equal to zero, and use Z∞ to identify a second index set (“C”), the indices of C being the (i,j) indices that are in A but not Z∞;
identify a third index set (“Cn”), the indices of Cn being the N indices of C which have the lowest λ values, where λ equals
P
fm
(
i
,
j
)
k
P
m
(
i
,
j
)
k
,
where N is a number for which the following is true:
FAR Z ∞ ∪C N =1−Σ (i,j)εZ ∞ P fm ( i,j )−Σ (i,j)εC N P fm ( i,j )≦FAR
determine indices of the specimen and compare the specimen's indices to Cn; and
if the specimen indices are in Cn, then accept the specimen as being authentic.
2. The method of claim 1 , wherein identifying Cn includes:
arranging the (i,j) indices of C such that
P
fm
(
i
,
j
)
k
P
m
(
i
,
j
)
k
>=
P
fm
(
i
,
j
)
k
+
1
P
m
(
i
,
j
)
k
+
1
to provide an arranged C index;
identifying the first N indices (i,j) of the arranged C index as being in Cn.
3. The method of claim 1 , wherein if the specimen indices are not in Cn, then reject the specimen as being not authentic.
4. The method of claim 1 , wherein Pfm(i,j) is similar to a Neyman-Pearson Lemma probability partition array.
5. The method of claim 1 , wherein Pm(i,j) is similar to a Neyman-Pearson Lemma probability partition array.
6. The method of claim 1 , wherein each object includes information corresponding to at least two biometric samples taken from an individual.
7. The method of claim 6 , wherein the information pieces are scores describing features of the biometric samples.
8. The method of claim 7 , wherein at least one of the scores is provided by a biometric reader device.
9. A computer readable memory device having stored thereon instructions that are executable by a computer to decide whether to accept a specimen, the instructions causing a computer to:
provide a data set having information pieces about objects, each object having a number of modalities, the number being at least two:
determine a first probability partition array (“Pm(i,j)”), the Pm(i,j) being comprised of probability values for information pieces in the data set, each probability value in the Pm(i,j) corresponding to the probability of an authentic match;
determine a second probability partition array (“Pfm(i,j)”), the Pfm(i,j) being comprised of probability values for information pieces in the data set, each probability value in the Pfm(i,j) corresponding to the probability of a false match;
identify a first index set (“A”), the indices in set A being the (i,j) indices that have values in both Pfm(i,j) and Pm(i,j);
identify a no-match zone (“Z∞”) that includes the indices of set A for which Pm(i,j) is equal to zero, and use Z∞ to identify a second index set (“C”), the indices of C being the (i,j) indices that are in A but not Z∞;
identify a third index set (“Cn”), the indices of Cn being the N indices of C which have the lowest λ values, where λ equals
P
fm
(
i
,
j
)
k
P
m
(
i
,
j
)
k
,
where N is a number for which the following is true:
FAR Z ∞ ∪C N =1−Σ (i,j)εZ ∞ P fm ( i,j )−Σ (i,j)εC N P fm ( i,j )≦FAR
determine indices of the specimen and compare the specimen's indices to Cn; and
if the specimen indices are in Cn, then accept the specimen as being authentic.
10. The memory device of claim 9 , wherein identifying Cn includes:
arranging the (i,j) indices of C such that
P
fm
(
i
,
j
)
k
P
m
(
i
,
j
)
k
>=
P
fm
(
i
,
j
)
k
+
1
P
m
(
i
,
j
)
k
+
1
to provide an arranged C index;
identifying the first N indices (i,j) of the arranged C index as being in Cn.
11. The memory device of claim 9 , wherein if the specimen indices are not in Cn, then the computer is instructed to reject the specimen as being not authentic.
12. The memory device of claim 9 , wherein Pfm(i,j) is similar to a Neyman-Pearson Lemma probability partition array.
13. The memory device of claim 9 , wherein Pm(i,j) is similar to a Neyman-Pearson Lemma probability partition array.
14. The memory device of claim 9 , wherein each object includes information corresponding to at least two biometric samples taken from an individual.
15. The memory device of claim 14 , wherein the information pieces are scores describing features of the biometric samples.
16. The memory device of claim 15 , wherein at least one of the scores is provided by a biometric reader device.