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Improving identification by pruning: A case study on face recognition and body soft biometric

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2 Author(s)
Velardo, C. ; Eurecom, Sophia Antipolis, France ; Dugelay, J.

We investigate body soft biometrics capabilities to perform pruning of a hard biometrics database improving both retrieval speed and accuracy. Our pre-classification step based on anthropometric measures is elaborated on a large scale medical dataset to guarantee statistical meaning of the results, and tested in conjunction with a face recognition algorithm. Our assumptions are verified by testing our system on a chimera dataset. We clearly identify the trade off among pruning, accuracy, and mensuration error of an anthropomeasure based system. Even in the worst case of ±10% biased anthropometric measures, our approach improves the recognition accuracy guaranteeing that only half database has to be considered.

Published in:

Image Analysis for Multimedia Interactive Services (WIAMIS), 2012 13th International Workshop on

Date of Conference:

23-25 May 2012

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