Multimodal fusion decision logic system using copula model
First Claim
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1. A method of deciding whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the method being executed by a computer processor, comprising:
- provide 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;
provide 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 second index set (“
Z∞
”
), the indices of Z∞
being the (i,j) indices in set A where both Pfm(i,j) is larger than zero and Pm(i,j) is equal to zero;
determine FARZ∞
, where FARZ∞
=1−
Σ
(i,j)∈
Z∞
Pfm(i,j);
compare FARZ∞
to a desired false-acceptance-rate (“
FAR”
);
if FARZ∞
is greater than the desired false-acceptance-rate, then reject the data set.
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Abstract
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.
36 Citations
18 Claims
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1. A method of deciding whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the method being executed by a computer processor, comprising:
-
provide 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;provide 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 second index set (“
Z∞
”
), the indices of Z∞
being the (i,j) indices in set A where both Pfm(i,j) is larger than zero and Pm(i,j) is equal to zero;determine FARZ∞
, where FARZ∞
=1−
Σ
(i,j)∈
Z∞
Pfm(i,j);compare FARZ∞
to a desired false-acceptance-rate (“
FAR”
);if FARZ∞
is greater than the desired false-acceptance-rate, then reject the data set. - View Dependent Claims (2, 3)
-
-
4. A method of deciding whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the method being executed by a computer processor, comprising:
-
provide 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;provide 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 A being the (i,j) indices that have values in both Pfm(i,j) and Pm(i,j);identify a second index set (“
Z∞
”
), the indices of Z∞
being the (i,j) indices of A where Pm(i,j) is equal to zero;identify a third index set (“
C”
), the indices of C being the (i,j) indices that are in A but not Z∞
,arrange the (i,j) indices of C such that - View Dependent Claims (5, 6)
-
-
7. A computer readable memory device having stored thereon instructions that are executable by a computer to decide whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the instructions causing a computer to:
-
(a) identify a first index set (“
A”
), the indices in set A being the (i,j) indices that have values in both Pm(i,j) and Pfm(i,j), Pm(i,j) being a probability partition array 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, and Pfm(i,j) being a probability partition array 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;(b) identify a second index set (“
Z∞
”
), the indices of Z∞
being the (i,j) indices in set A where both Pfm(i,j) is larger than zero and Pm(i,j) is equal to zero;(c) determine FARZ∞
, where FARZ∞ =1−
Σ
(i,j)∈
Z∞ Pfm(i,j);(d) compare FARZ∞
to a desired false-acceptance-rate (“
FAR”
);(e) if FARZ∞
is greater than the desired false-acceptance-rate, then reject the data set. - View Dependent Claims (8, 9)
-
-
10. A computer readable memory device having stored thereon instructions that are executable by a computer to decide whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the instructions causing a computer to:
-
(a) identify a first index set (“
A”
), the indices in A being the (i,j) indices that have values in both Pm(i,j) and Pfm(i,j), wherein the Pm(i,j) is a probability partition array 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, and Pfm(i,j) is a probability partition array 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(b) identify a second index set (“
Z∞
”
), the indices of Z∞
being the (i,j) indices of A where Pm(i,j) is equal to zero;(c) identify a third index set (“
C”
), the indices of C being the (i,j) indices that are in A but not Z∞
;(d) arrange the (i,j) indices of C such that - View Dependent Claims (11, 12)
-
-
13. A method of deciding whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the method being executed by a computer processor, comprising:
-
provide 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;provide 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);execute at least one of the following; a) identify a first no-match zone (“
Z1∞
”
) that includes at least the indices of set A for which both Pfm(i,j) is larger than zero and Pm(i,j) is equal to zero, and use Z1∞
determine FARZ∞
, where FARZ∞ =1−
Σ
(i,j)∈
Z∞ Pfm(i,j), and compare FARZ∞
to a desired false-acceptance-rate (“
FAR”
), and if FARZ∞
greater than the desired false-acceptance-rate, then reject the data set;b) identify a second no-match zone (“
Z2∞
”
) that includes the indices of set A for which Pm(i,j) is equal to zero, and use Z2∞
identify a second index set (“
C”
), the indices of C being the (i,j) indices that are in A but not Z2∞ and
arrange the (i,j) indices of C such that - View Dependent Claims (14, 15)
-
-
16. A computer readable memory device having stored thereon instructions that are executable by a computer to decide whether a data set is acceptable for making a decision, the data set being comprised of information pieces about objects, each object having a number of modalities, the number being at least two, the instructions causing a computer to:
-
identify a first index set (“
A”
), the indices in A being the (i,j) indices that have values in both Pm(i,j) and Pfm(i,j), wherein the Pm(i,j) is a probability partition array 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, and Pfm(i,j) is a probability partition array 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;execute at least one of the following; a) identify a first no-match zone (“
Z1∞
”
) that includes at least the indices of set A for which both Pfm(i,j) is larger than zero and Pm(i,j) is equal to zero, and use Z1∞
determine FARZ∞
, where FARZ∞ 1−
Σ
(i,j)∈
Z∞ Pfm(i,j), and compare FARZ∞
to a desired false-acceptance-rate (“
FAR”
), and if FARZ∞
is greater than the desired false-acceptance-rate, then reject the data set;b) identify a second no-match zone (“
Z2∞
”
) that includes the indices of set A for which Pm(i,j) is equal to zero, and use Z2∞
identify a second index set (“
C”
), the indices of C being the (i,j) indices that are in A but not Z2∞
, and arrange the (i,j) indices of C such that - View Dependent Claims (17, 18)
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Specification
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Current AssigneeQualcomm, Inc.
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Original AssigneeUltra-Scan Corporation (Qualcomm, Inc.)
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InventorsKiefer, Fred W.
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Primary Examiner(s)Vincent; David R
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Assistant Examiner(s)Brown, Jr.; Nathan H
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Application NumberUS11/876,521Publication NumberTime in Patent Office624 DaysField of Search706/15, 382/115, 382/116US Class Current706/15CPC Class CodesG06N 20/00 Machine learningG06N 5/02 Knowledge representation; S...
