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Erschienen in:
Application of Image Classification for Fine-Grained Nudity Detection Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Efficient 3D Face Recognition in Uncontrolled Environment

verfasst von : Yuqi Ding, Nianyi Li, S. Susan Young, Jinwei Ye

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Face recognition in an uncontrolled environment is challenging as body movement and pose variation can result in missing facial features. In this paper, we tackle this problem by fusing multiple RGB-D images with varying poses. In particular, we develop an efficient pose fusion algorithm that frontalizes the faces and combines the multiple inputs. We then introduce a new 3D registration method based on the unified coordinate system (UCS) to compensate for pose and scale variations and normalize the probe and gallery face. To perform 3D face recognition, we train a Support Vector Machine (SVM) with both 2D color and 3D geometric features. Experimental results on a RGB-D dataset show that our method can achieve a high recognition rate and is robust in the presence of pose and expression variations.

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Vorheriges Kapitel Age Group and Gender Classification of Unconstrained Faces
Nächstes Kapitel Pupil Center Localization Using SOMA and CNN
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Metadaten
Titel
Efficient 3D Face Recognition in Uncontrolled Environment
verfasst von
Yuqi Ding
Nianyi Li
S. Susan Young
Jinwei Ye
Copyright-Jahr
2019
Buch
Advances in Visual Computing

Print ISBN: 978-3-030-33719-3

Electronic ISBN: 978-3-030-33720-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-33720-9_33

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