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The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

IFEPE: On the Impact of Facial Expression in Head Pose Estimation

Authors : Carmen Bisogni, Chiara Pero

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

The Head Pose Estimation (HPE) is the study of the angular rotations of the head along the Pitch, Yaw, and Roll axes. Widely used in facial involving methods, as face frontalization, driver attention and best surveillance frame selection, is strongly related to facial features. In this study we examine the impact of facial expressions (FE) on HPE and, in particular, we put in relation the axis more affected by the error when a specific facial expression is observable. The HPE method chosen for this purpose is based on the Partitioned Iterated Function System (PIFS). For its construction this method is dependent on the facial appearance and self-similarity. Basing on this, and using a FER network, we observed that there is an evident relation between facial expressions and pose errors. This relation go thought the facial keypoints distances and can be discriminated along the three axes, by providing an estimate of the percentages of variation in errors related to a percentage of variation in distances.

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Metadata
Title
IFEPE: On the Impact of Facial Expression in Head Pose Estimation
Authors
Carmen Bisogni
Chiara Pero
Copyright Year
2021
Book
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68821-9_41

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