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Spatiotemporal Stacked Sequential Learning for Pedestrian Detection Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Person Enrollment by Face-Gait Fusion

Authors : Javier Ortells, Ramón A. Mollineda

Published in: Pattern Recognition and Image Analysis

Publisher: Springer International Publishing

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Abstract

This paper studies the problem of automatic person enrollment based on face-gait fusion within a context of anonymous identification. Enrollment should determine whether an observed subject has been seen before or not. Traditionally, this process has been embedded into a major identification system and its potential has been undervalued. This work claims that enrollment can be considered as a task in itself, and that there are real applications that can benefit from exclusively managing it. To this end, it is shown that the enrollment error model is different from that of anonymous identification. Enrollment experiments, conducted over three types of random permutations of probe samples, showed the benefits of face-gait fusion over single biometrics.

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Footnotes
1
Note that the converse holds when scores encode similarities (rather than distances).
 
2
The reader will find more details on methods and datasets in next sections.
 
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Metadata
Title
Person Enrollment by Face-Gait Fusion
Authors
Javier Ortells
Ramón A. Mollineda
Copyright Year
2015
Book
Pattern Recognition and Image Analysis

Print ISBN: 978-3-319-19389-2

Electronic ISBN: 978-3-319-19390-8

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-19390-8_54

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