How can one expect a biometric system that relies on a single enrolment sample to cope with all the variability possibly encountered during the operational phase? To maintain good performance, one way is to combine multiple biometric traits. This approach which is known as multimodal biometrics can be shown theoretically that this approach leads to improved recognition accuracy. This lecture will explore some aspects of multmodal biometric adaptation, ranging from the use of quality measures, user-specific statistics and cohort information to the new exciting development of template-update and adaptive threshold/score normalization techniques.
The goal of the lecture is to show how the above problems can be solved using machine learning techniques as fundamental building blocks. For instance, it will be shown how a clustering algorithm can be combined with a generative/discriminative classifier to form a mixture of linear classifiers that results in the state-of-the-art classifier for quality-based fusion. Another example is how the cohort information can be modelled first by regression and then solved as a classification problem. [Go to the full record in the library's catalogue]
This video is presented here with the permission of the speakers.
Any downloading, storage, reproduction, and redistribution are strictly prohibited
without the prior permission of the respective speakers.
Go to Full Disclaimer.
Full Disclaimer
This video is archived and disseminated for educational purposes only. It is presented here with the permission of the speakers, who have mandated the means of dissemination.
Statements of fact and opinions expressed are those of the inditextual participants. The HKBU and its Library assume no responsibility for the accuracy, validity, or completeness of the information presented.
Any downloading, storage, reproduction, and redistribution, in part or in whole, are strictly prohibited without the prior permission of the respective speakers. Please strictly observe the copyright law.
