Client threshold prediction in biometric signature recognition by means of Multiple Linear Regression and its use for score normalization

• A new method for client threshold prediction in biometric signature is proposed.
• The proposal is based on Multiple Linear Regression, a well founded statistical tool.
• New robust parameters, not used before, have been successfully included in the model.
• The prediction model is optimized for each working point.
• The predicted threshold is used in score normalization improving the state of the art.

Biometric person authentication has become an important area of fieldwork both for research and commercial purposes in the last few years. The development of the technology, now ready for practical applications, has encouraged the scientific community to focus on practical issues. In this sense, a key question is the decision threshold estimation. Biometric authentication is a pattern recognition problem where a final decision (identity accepted/rejected) must be taken; so, to set a correct decision threshold is essential, since the best system becomes useless if an inaccurate decision threshold is fixed. This work focuses on this subject for biometric systems based on manuscript signatures. The decision threshold can be client (signatory) dependent or the same for all (common threshold). In this paper, new approaches for both problems are shown. A new solution, based on the Multiple Linear Regression model, is proposed for client dependent decision threshold estimation or prediction. The state of the art shows that only independent variables based on the Gaussian scores distribution supposition have been used. Here, new robust parameters, not based on that supposition, have been successfully included in the model. This proposal has been evaluated by means of both a statistical validation and a performance comparison with the state of the art. When a common threshold is used, the problem is to normalize the client scores. A new proposal for this task is also shown, based on the use of the predicted client threshold. Both proposals have been multi-working point, multi-corpus and multi-classifier tested. Improvements from 12% to 57% have been achieved with respect to the state of the art in threshold prediction, while these improvements are from 15% to 40% in the score normalization task.