Identifier comparison
View Patent ↗A method of comparing a first representation of an identifier with a second representation of an identifier is provided. The method includes providing an expression of the first representation, such as a fingerprint, and considering the expression of the first representation against a probability distribution based on the variation in the expression between different example representations of the second representations, to provide a first consideration. The method also provided for considering the expression of the first representations against a probability distribution based on the variation in the expression between different population representations, to provide a second consideration. By using the first consideration and second consideration together it is possible to provide a measure of comparison between the first representation and the second representation.
1. A method of comparing a first representation of an identifier with a second representation of an identifier, the method including:
providing an expression of the first representation;
providing an expression of the second representation;
providing a plurality of example representations of the second representation;
providing an expression of each of the plurality of example representations;
providing a probability distribution based on the expressions of the plurality of example representations of the second representations;
considering the expression of the first representation against the probability distribution for the example representations, to provide a first consideration;
providing a plurality of population representations from different origins;
providing an expression of each of the plurality of population representations;
providing a probability distribution based on the expressions of the plurality of population representations;
considering the expression of the second representation against the probability distribution for the population examples, to provide a second consideration;
using the first consideration and second consideration to provide a measure of comparison between the first representation and the second representation.
2. A method according to claim 1 in which the considering of the expression of the first representation against the probability distribution for the example representations, to provide a first consideration, includes considering the expression of the first representation and the expression of the second representation.
3. A method according to claim 1 in which the consideration is of the distance between the expression of the first representation and the expression of the second representation.
4. A method according to claim 1 in which the considering of the expression of the expression of the second representation against the probability distribution for the population examples, to provide a second consideration, includes considering the expression of the second representation and the expression of the first representation.
5. A method according to claim 4 in which the consideration is of the distance between the expression of the second representation and the expression of the first representation.
6. A method according to claim 1 in which the considering of the expression of the first representation against a probability distribution, for different example representations of the second representation, forms the numerator in the use of the first consideration and second consideration.
7. A method according to claim 6 in which the considering of the expression involves finding the frequency for that expression value in the probability distribution.
8. A method according to claim 1 in which the probability distribution based on the expressions of the example representations is arrived at by estimating from a database of example representations of an identifier taken from the same source.
9. A method according to claim 1 in which the probability distribution based on the expressions of the population examples are arrived at by estimating from a database of population representations of an identifier from different sources.
10. A method according to claim 1 in which the first consideration and second consideration are used to evaluate a hypothesis, the hypothesis including as the first consideration, that the first representation and the second representation are from the same source.
11. A method according to claim 1 in which the first consideration and second consideration are used to evaluate a hypothesis, the hypothesis including as the second consideration, that the first representation and the second representation are from different sources.
12. A method according to claim 1 in which the use of the first consideration and second consideration is to evaluate a prosecution hypothesis and a defence hypothesis.
13. A method according to claim 1 in which the use of the first consideration and second consideration is to evaluate:
LR
=
Pr
(
fv
s
,
fv
m
❘
H
p
)
Pr
(
fv
s
,
fv
m
❘
H
d
)
where fv s denotes a feature vector which comes from the second representation when conditioned on Hp and from an unknown source when conditioned on H d , fv m denotes a feature vector originating from the first representation.
14. A method according to claim 1 in which the use of the first consideration and the second consideration is to provide a measure of the strength of link between the first representation and the second representation in the form of a likelihood ratio.
15. A method according to claim 14 in which an indication as to whether the first representation is likely to have the same source as the second representation is provided.
16. A method according to claim 1 in which the measure of comparison is a likelihood ratio and the probability for the numerator in the likelihood ratio is stated as:—
Num=Σ{ Pr ( d ( fv s,c ,fv m,c )| fv s,d ,fv m,d ,H p ): for all fv s,d and fv m,d such that fv s,d =fv m,d }
where
fv means feature vector, c means continuous, d means discrete, m means mark and s means suspect and therefore:
fv m,c : continuous data of the feature vector from the mark
fv m,d : discrete data of the feature vector from the mark
fv s,c : discrete data of the feature vector from the suspect
fv s,d : discrete data of the feature vector from the suspect
d(fv s,c , fv m,c ) is the distance measured between the continuous data of the two feature vectors from the mark and the suspect
H p is the prosecution hypothesis, that is the two feature vectors originate from the same source.
17. A method according to claim 1 in which the measure of comparison is a likelihood ratio and the probability for the denominator of the likelihood ratio is stated as:—
Den=Σ{ Pr ( d ( fv s,c ,fv m,c )| fv s,d ,fv m,d ,H d ) Pr ( fv m,d |H d ): for all fv s,d and fv m,d such that fv s,d =fv m,d }
where
fv means feature vector, c means continuous, d means discrete, m means mark and s means suspect and therefore:
fv m,c : continuous data of the feature vector from the mark
fv m,d : discrete data of the feature vector from the mark
fv s,c : discrete data of the feature vector from the suspect
fv s,d : discrete data of the feature vector from the suspect
d(fv s,c , fv m,c ) is the distance measured between the continuous data of the two feature vectors from the mark and the suspect
H d is the defence hypothesis, that is the two feature vectors originate from different sources.
18. A method according to claim 1 in which the indication and probability distributions provide a measure of the strength of link between the first representation and the second representation, and the probability for the numerator of the likelihood ratio is stated as:
Num= Pr ( d ( fv s ,fv m )| H p )
where
d(fv s , fv m ) is the distance measured between continuous data of the two feature vectors from the mark and suspect;
H p is the prosecution hypothesis, that is the two vectors originate from the same source.
19. A method according to claim 1 in which the indication and probability distributions provide a measure of the strength of link between the first representation and the second representation, and the probability for the denominator of the likelihood ratio is stated as:
Den= Pr ( d ( fv s ,fv m )| H d )
where
H d is the defence hypothesis, that is the two vectors originate from different sources.