Minimal Redundancy Maximal Relevance Criterion-based Multi-biometric Feature Selection

Abstract

Multimodal biometrics are always adopted to improve the recognition performance of single modality biometric systems. Besides introducing more discriminating power to the biometric system, integrating multiple modalities also leads to the curse of dimensionality problem. In this paper, we engage the minimal redundancy maximal relevance criterion to reduce the dimensionality of the feature vector. The minimal redundancy maximal relevance criterion is a feature selection criterion that aims to retain the most relevant elements while discarding the other redundant elements. Our experiments show that, with only 15% of the original feature length, minimal redundancy maximal relevance criterion-based features are able to perform similarly well or even better than the baseline results.