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2021 | OriginalPaper | Buchkapitel

Subject Identification Across Large Expression Variations Using 3D Facial Landmarks

verfasst von : SK Rahatul Jannat, Diego Fabiano, Shaun Canavan, Tempestt Neal

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

In this work, we propose to use 3D facial landmarks for the task of subject identification, over a range of expressed emotion. Landmarks are detected, using a Temporal Deformable Shape Model and used to train a Support Vector Machine (SVM), Random Forest (RF), and Long Short-term Memory (LSTM) neural network for subject identification. As we are interested in subject identification with large variations in expression, we conducted experiments on 3 emotion-based databases, namely the BU-4DFE, BP4D, and BP4D+ 3D/4D face databases. We show that our proposed method outperforms current state of the art methods for subject identification on BU-4DFE and BP4D. To the best of our knowledge, this is the first work to investigate subject identification on the BP4D+, resulting in a baseline for the community.

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Nächstes Kapitel 3D Human Pose Estimation Based on Multi-Input Multi-Output Convolutional Neural Network and Event Cameras: A Proof of Concept on the DHP19 Dataset

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Titel: Subject Identification Across Large Expression Variations Using 3D Facial Landmarks
verfasst von: SK Rahatul Jannat
Diego Fabiano
Shaun Canavan
Tempestt Neal
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68762-5

Electronic ISBN: 978-3-030-68763-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68763-2_1

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