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Face and Facial Expression Recognition from Real World Videos

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Cover of 'Face and Facial Expression Recognition from Real World Videos'

Table of Contents

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    Book Overview
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    Chapter 1 Probabilistic Elastic Part Model for Real-World Face Recognition
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    Chapter 2 Local Feature Evaluation for a Constrained Local Model Framework
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    Chapter 3 Multimodal Face Recognition for Profile Views Based on SIFT and LBP
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    Chapter 4 Learning the Face Shape Models for Facial Landmark Detection in the Wild
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    Chapter 5 Multimodal Facial Expression Recognition Based on 3D Face Reconstruction from 2D Images
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    Chapter 6 Facial Expression Recognition Using Facial Graph
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    Chapter 7 Measurement of Facial Dynamics for Soft Biometrics
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    Chapter 8 Personalized Face Reference from Video: Key-Face Selection and Feature-Level Fusion
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    Chapter 9 Scalable Face Retrieval by Simple Classifiers and Voting Scheme
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    Chapter 10 Positive/Negative Emotion Detection from RGB-D Upper Body Images
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    Chapter 11 Face and Facial Expression Recognition from Real World Videos
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    Chapter 12 A Comparative Evaluation of Regression Learning Algorithms for Facial Age Estimation
Attention for Chapter 5: Multimodal Facial Expression Recognition Based on 3D Face Reconstruction from 2D Images
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Citations

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Summary Dimensions citations
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Chapter title
Multimodal Facial Expression Recognition Based on 3D Face Reconstruction from 2D Images
Chapter number 5
Book title
Face and Facial Expression Recognition from Real World Videos
Published by
Springer, Cham, January 2015
DOI 10.1007/978-3-319-13737-7_5
Book ISBNs
978-3-31-913736-0, 978-3-31-913737-7
Authors

Ali Moeini, Hossein Moeini

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 22%
Researcher 2 22%
Lecturer > Senior Lecturer 1 11%
Student > Master 1 11%
Unknown 3 33%
Readers by discipline Count As %
Computer Science 4 44%
Engineering 2 22%
Unknown 3 33%

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