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Active Shape Model vs. Deep Learning for Facial Emotion Recognition in Security Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models

Authors : Barbara Villarini, Athanasios Gkelias, Vasilios Argyriou

Published in: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Publisher: Springer International Publishing

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Abstract

Face recognition in presence of illumination changes, variant pose and different facial expressions is a challenging problem. In this paper, a method for 3D face reconstruction using photometric stereo and without knowing the illumination directions and facial expression is proposed in order to achieve improvement in face recognition. A dimensionality reduction method was introduced to represent the face deformations due to illumination variations and self shadows in a lower space. The obtained mapping function was used to determine the illumination direction of each input image and that direction was used to apply photometric stereo. Experiments with faces were performed in order to evaluate the performance of the proposed scheme. From the experiments it was shown that the proposed approach results very accurate 3D surfaces without knowing the light directions and with a very small differences compared to the case of known directions. As a result the proposed approach is more general and requires less restrictions enabling 3D face recognition methods to operate with less data.

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previous chapter The SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Pain- and Emotion-Recognition System
next chapter Recursively Measured Action Units
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Metadata
Title
Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models
Authors
Barbara Villarini
Athanasios Gkelias
Vasilios Argyriou
Copyright Year
2017
Book
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Print ISBN: 978-3-319-59258-9

Electronic ISBN: 978-3-319-59259-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59259-6_12

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