On the Use of Score Ratio with Distance-Based Classifiers in Biometric Signature Recognition

Abstract

Biometric user verification or authentication is a pattern recognition problem that can be stated as a basic hypothesis test: X is from client C ($H_0$) vs. X is not from client C ($H_1$), where X is the biometric input sample (face, fingerprint, etc.). When probabilistic classifiers are used (e.g., Hidden Markov Models), the decision is typically performed by means of the likelihood ratio: ${P(X/H_0)}/{P(X/H_1)}$. However, as far as we know, this ratio is not usually performed when distance-based classifiers (e.g., Dynamic Time Warping) are used. Following that idea, we propose, here, to perform the decision based not only on the score (“score” being the classifier output) supposing X is from the client ($H_0$), but also using the score supposing X is not from the client ($H_1$), by means of the ratio between both scores: the score ratio. A first approach to this proposal can be seen in this work, showing that to use the score ratio can be an interesting technique to improve distance-based biometric systems. This research has focused on the biometric signature, where several state of the art systems based on distance can be found. Here, the score ratio proposal is tested in three of them, achieving great improvements in the majority of the tests performed. The best verification results have been achieved with the use of the score ratio, improving the best ones without the score ratio by, on average, 24 %.