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ICASSP
2008
IEEE

Mutual features for robust identification and verification

13 years 10 months ago
Mutual features for robust identification and verification
Noisy or distorted video/audio training sets represent constant challenges in automated identification and verification tasks. We propose the method of Mutual Interdependence Analysis (MIA) to extract “mutual features” from a high dimensional training set. Mutual features represent a class of objects through a unique direction in the span of the inputs that minimizes the scatter of the projected samples of the class. They capture invariant properties of the object class and can therefore be used for classification. The effectiveness of our approach is tested on real data from face and speaker recognition problems. We show that “mutual faces” extracted from the Yale database are illumination invariant, and obtain identification error rates of 2.2% in leave-one-out tests for differently illuminated images. Also, “mutual speaker signatures” for text independent speaker verification achieve state-of-theart equal error rates of 6.8% on the NTIMIT database.
Heiko Claussen, Justinian Rosca, Robert I. Damper
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Heiko Claussen, Justinian Rosca, Robert I. Damper
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