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Multimodal Biometrics with Auxiliary Information: Quality, User-specific, Cohort information and beyond

Dept. of Computer Science (January 9, 2017)

CONFERENCE / SYMPOSIUM : IAPR/IEEE Winter School on Biometrics

Biometric Data Analysis
Multimodal Biometrics with Auxiliary Information: Quality, User-specific, ...
2D and 3D Face Recognition
Human Identification via Gait Recognition
Hand-Based Biometrics
Recent Progress of Iris Recognition
Mobile Biometrics: Trends and Issues
Deep Learning in Face Analysis
Fingerprint Recognition
Machine Learning for Person Identification
Soft Biometrics and Continuous Authentication
Secure Scalable CCTV, Mobile, and Wearable Video Face Recognition
Biometric Indexing
Face Recognition System Security: Template Protection and Anti-spoofing
LENGTH : 97 min.
ACCESS : Open to all
SUMMARY : 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]

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