Abstract—Personal verification system that uses a single
biometric trait often faces numerous limitations such as noisy
sensor data, non-universality, non-distinctiveness and spoof
attack. These limitations can be overcome by multimodal
biometric systems that consolidate the evidence presented by
multiple biometric sources and typically has better recognition
performance compared to systems based on a single biometric
modality. This study proposes fusion of face and fingerprint for
robust recognition system. The integration is performed at the
matching score level. The matching tasks for both modalities
are carried out by using support vector machines (SVM) as the
classifier. Experiments on face expression and fingerprint
database show that the performances of multimodal biometric
system provide better recognition compared to single biometric
modality. Based on the fusion techniques evaluated,
trait-specific weight was found to be highly effective than the
sum rule-based fusion. Equal error rate (EER) percentage for
face- only and fingerprint- only systems are 2.50% and 5.56%,
respectively, while the EER for system using sum rule- based
fusion and system using trait-specific weights are 0.833% and
Index Terms—Multi-modal, sum-rule and trait-specific, face and fingerprint biometrics.
Norsalina Hassan was with Intelligent Biometric Group, School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia. She is currently with the Mathematic, Science & Computer Department, Politeknik Seberang Perai, Jalan Permatang Pauh, 13500 Permatang Pauh, Pulau Pinang, Malaysia (e-mail: email@example.com).
Dzati Athiar Ramli and Shahrel Azmin Suandi are with the Intelligent Biometric Group, School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Norsalina Hassan, Dzati Athiar Ramli, and Shahrel Azmin Suandi, "Fusion of Face and Fingerprint for Robust Personal Verification System," International Journal of Machine Learning and Computing vol.4, no. 4, pp. 371-375, 2014.