Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 3; pp. 450 - 455
• Main Authors: Snelick, R., Uludag, U., Mink, A., Indovina, M., Jain, A.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.03.2005; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Authentication; Biometrics; Biometry - methods; Computer science; control theory; systems; Computer Simulation; Dermatoglyphics; Estimation error; Exact sciences and technology; face; Face - anatomy & histology; Feature extraction; fingerprint; Fingerprint recognition; Fingerprints; fusion; Gain; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Index Terms- Multimodal biometrics; Information Storage and Retrieval - methods; Intelligence; Large-scale systems; matching score; normalization; Pattern analysis; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Performance gain; Reproducibility of Results; Sensitivity and Specificity; Spatial databases; State of the art; Subtraction Technique; Support vector machines; System performance; Biometrics; fusion; Fingerprint; face; matching score; Melting; normalization; Authentication; Standardization; Matching task; Multimodal biometrics
• DOI: 10.1109/TPAMI.2005.57
• ISSN: 0162-8828

We examine the performance of multimodal biometric authentication systems using state-of-the-art commercial off-the-shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.