Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Face recognition using the mixture-of-eigenfaces method
Pattern Recognition Letters
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of appearance-based face recognition algorithms
Pattern Analysis & Applications
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint verification by fusion of optical and capacitive sensors
Pattern Recognition Letters
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Image Pattern Recognition: Synthesis and Analysis in Biometrics (Series in Machine Perception & Artifical Intelligence)
Journal of Cognitive Neuroscience
Score normalization in multimodal biometric systems
Pattern Recognition
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Combining face and iris biometrics for identity verification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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
Exploiting global and local decisions for multimodal biometrics verification
IEEE Transactions on Signal Processing - Part II
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
A fusion approach to unconstrained iris recognition
Pattern Recognition Letters
Rank based hybrid multimodal fusion using PSO
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Axis-Parallel dimension reduction for biometric research
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
ACM Computing Surveys (CSUR)
Computational and space complexity analysis of SubXPCA
Pattern Recognition
Applied Computational Intelligence and Soft Computing
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In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher's linear discriminant methods for individual matchers (face, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.