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2019 | OriginalPaper | Buchkapitel

Robust Classification of Head Pose from Low Resolution Images

verfasst von : Mohammad Khaki, Issa Ayoub, Abbas Javadtalab, Hussein Al Osman

Erschienen in: Computer Analysis of Images and Patterns

Verlag: Springer International Publishing

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Abstract

We propose a method for the coarse classification of head pose from low-resolution images. We devise a mechanism that uses a cascade of three binary Support Vector Machines (SVM) classifiers. We use two sets of appearance features, Similarity Distance Map (SDM) and Gabor Wavelet (GW) as input to the SVM classifiers. For training, we employ a large dataset that combines five publicly available databases. We test our approach with cross-validation using the eight databases and on videos we collected in a lab experiment. We found a significant improvement in the results achieved by the proposed method over existing schemes. In the cross-validation test, we achieved a head pose detection accuracy of 98.60%. Moreover, we obtained a head pose detection accuracy of 93.76% for high-resolution and 89.81% for low-resolution videos collected in the lab under loosely constrained conditions.

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Titel: Robust Classification of Head Pose from Low Resolution Images
verfasst von: Mohammad Khaki
Issa Ayoub
Abbas Javadtalab
Hussein Al Osman
Verlag: Springer International Publishing
Buch: Computer Analysis of Images and Patterns
Print ISBN: 978-3-030-29887-6

Electronic ISBN: 978-3-030-29888-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-29888-3_43

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