Background and Objectives: There is high requirement of face and signature based multimodal biometric systems in various areas, such as banking, biometric systems and secured mobile phone operating systems. Few studies have been carried out in this area to enhance the performance of identification and authentication based on the fusion of those modalities. In multimodal biometric systems, the most common fusion approach is integration at the matching score level, but it is necessary to compare this strategy of fusion to the other strategies, like fusion at feature level. Our system combines these two biometric traits and provides better recognition performance compared with single biometric systems. Multimodal Authentication Systems: The first monomodal verification system is based on face verification using Gabor filters for feature extraction. The second system is based on online signature verification using Nalwa's method. The classification is released using the Cosine Mahalano-bis distance. Due to its efficiency, we used max-of-scores strategy to fuse face and online signature scores. The second proposed system is based on fusion at the feature level. Results and Conclusions: The performance of feature-level fusion and max-of-scores fusion techniques using face and online signature modalities were evaluated on ORL face database and the QU signature database. The lowest equal error rate is obtained by using a fusion strategy based on max-of-monomodal systems scores. Additionally, feature-level fusion based methods demonstrate a low equal error rate compared with the monomodal systems and have not been affected by the increase in the features vector dimension in term of time of verification; on the contrary, the fusion at the score level is clearly affected and it takes more in-time verification because it's necessary to get scores from each biometric trait before the fusion step.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error