| Paper: | MLSP-P2.12 | ||
| Session: | Bioinformatics and Biomedical Applications | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | EXPLOITING GENERAL KNOWLEDGE IN USER-DEPENDENT FUSION STRATEGIES FOR MULTIMODAL BIOMETRIC VERIFICATION | ||
| Authors: | Julián Fiérrez-Aguilar; Universidad Politécnica de Madrid | ||
| Daniel Garcia-Romero; Universidad Politécnica de Madrid | |||
| Javier Ortega-García; Universidad Politécnica de Madrid | |||
| Joaquín González-Rodríguez; Universidad Politécnica de Madrid | |||
| Abstract: | In this paper, a novel strategy for combining general and user-dependent knowledge in a multimodal biometric verification system is presented. It is based on SVM classifiers and trade-off coefficients introduced in the standard SVM training problem. Experiments are reported on a bimodal biometric system based on fingerprint and on-line signature traits. A comparison between three fusion strategies, namely user-independent, user-dependent and the proposed adapted user-dependent, is carried out. As a result, the suggested approach outperformed the former ones. In particular, a highly remarkable relative improvement of 68% in the EER with respect to the user-independent approach is achieved. The severe and very common problem of training data scarcity in the user-dependent strategy is also relaxed by the proposed scheme, resulting in a relative improvement of 40% in the EER compared to the raw user-dependent strategy. | ||
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