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Erschienen in:
A Novel Data Representation Based on Dissimilarity Increments Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Dissimilarity Representations for Low-Resolution Face Recognition

verfasst von : Mairelys Hernández-Durán, Veronika Cheplygina, Yenisel Plasencia-Calaña

Erschienen in: Similarity-Based Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Low-resolution face recognition is a very difficult problem. In this setup, the training database or gallery contains high-resolution images, but the image to be recognized is of low resolution. Thus we are dealing with a resolution mismatch problem for training and test images. Standard face recognition methods fail in this setting, which suggests that current feature representation approaches are not adequate to cope with this problem. Therefore, we propose the use of dissimilarity representations based on different strategies, which differ in how images with different resolutions are compared, to solve the resolution mismatch problem. Experiments on four standard face datasets demonstrate that a strategy based on first down-scaling and afterwards up-scaling training images while up-scaling test images outperforms all the other approaches.

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Vorheriges Kapitel Introducing Negative Evidence in Ensemble Clustering Application in Automatic ECG Analysis
Nächstes Kapitel Deep Metric Learning Using Triplet Network
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Metadaten
Titel
Dissimilarity Representations for Low-Resolution Face Recognition
verfasst von
Mairelys Hernández-Durán
Veronika Cheplygina
Yenisel Plasencia-Calaña
Copyright-Jahr
2015
Buch
Similarity-Based Pattern Recognition

Print ISBN: 978-3-319-24260-6

Electronic ISBN: 978-3-319-24261-3

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-24261-3_6