Fusion of Multi-classifiers for Online Signature Verification Using Fuzzy Logic Inference

Compared to physiologically based biometric systems such as fingerprint, face, palm-vein and retina, behavioral based biometric systems such as signature, voice, gait, etc. are less popular and many of the research in these areas are still in their infancy. One of the reasons is due to the inconsistencies in human behavior which requires more robust algorithms in their developments. In this paper, an online signature verification system is proposed based on fuzzy logic inference. To ensure higher accuracy, the signature verification system is designed to include the fusion of multi classifiers, namely, the back propagation neural network algorithm and the Pearson correlation technique. A fuzzy logic inference engine is also designed to fuse two global features which are the time taken to sign and the length of the signature. The use of the fuzzy logic inference engine is to overcome the boundary limitations of fixed thresholds and overcome the uncertainties of thresholds for various users and to have a more human-like output. The system has been developed with a robust validation module based on Pearson’s correlation algorithm in which more consistent sets of signatures are enrolled. In this way, more consistent sets of training patterns are used for training. The results show that the incorporation of multi classifier fusion technique has improved the false rejection rate and false acceptance rate of the system as compared to the individual classifiers and the use of fuzzy logic inference module for the final decision helps to further improved the system performance.