Machine Learning
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
A comparative evaluation of fusion strategies for multimodal biometric verification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
The complete gabor-fisher classifier for robust face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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Face and signature based multimodal biometric systems are often required in various areas, such as banking biometric systems and secured mobile phone operating systems, among others. Our system combines these two biometric traits and provides better recognition performance compared with the systems based on a single biometric trait or modality. In multimodal biometric system, the most common fusion approach is integration at the matching score level because of the ease of combining and accessing the scores generated by different matchers. In this paper, we study the performance of a max-of-scores fusion technique based on the face and signature traits of a user. The experiments that were conducted on a database of 40 users indicate that the max-of-scores fusion-based method yields better authentication performance than single-face, single-signature, simple-sum or min-of-scores fusion-based biometric systems.