title: Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems.
abstract: 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.
authors.forename: R
authors.initials: R
authors.lastname: Snelick
authors.forename: U
authors.initials: U
authors.lastname: Uludag
authors.forename: A
authors.initials: A
authors.lastname: Mink
authors.forename: M
authors.initials: M
authors.lastname: Indovina
authors.forename: A
authors.initials: A
authors.lastname: Jain
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003878:Dermatoglyphics; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique