IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Guide to Biometrics
Robust Real-Time Face Detection
International Journal of Computer Vision
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
COMBINING FINGERPRINT, PALMPRINT AND HAND-SHAPE FOR USER AUTHENTICATION
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Journal of Cognitive Neuroscience
Personal Recognition Using Single-Sensor Multimodal Hand Biometrics
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Mixed group ranks: preference and confidence in classifier combination
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
Fusion of near infrared face and Iris biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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This paper presents an approach for optimizing both recognition and processing performance of a biometric system in identification mode. Multibiometric techniques facilitate bridging the gap between desired performance and current unimodal recognition rates. However, traditional parallel classifier combination techniques, such as Score sum, Borda count and Highest rank, cause further processing overhead, as they require a matching of the extracted sample with each template of the system for each feature. We examine a framework of serial combination techniques, which exploits ranking capabilities of individual features by reducing the set of possible matching candidates at each iteration, and we compare its performance with parallel schemes. Using this technique, both a reduction of misclassification and processing time in identification mode will be shown to be feasible for a single-sensor hand-based biometric system.