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CapsField: Light Field-Based Face and Expression Recognition in the Wild Using Capsule Routing.

Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 1, 2021

Summary

This study introduces CapsField, a novel deep learning method using light field cameras for face and expression recognition in real-world conditions. CapsField significantly outperforms existing methods on new "in the wild" datasets.

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Area of Science:

Computer Vision
Machine Learning
Biometrics

Background:

Light field (LF) cameras capture rich spatio-angular data, enhancing human-machine systems like biometrics and affective computing.
Previous LF applications were limited to constrained facial image analysis, not real-world scenarios.

Purpose of the Study:

To propose and evaluate CapsField, a deep learning solution for face and expression recognition using LF imaging in unconstrained environments.
To introduce the first "in the wild" LF face dataset and a complementary constrained dataset for robust evaluation.

Main Methods:

CapsField employs a convolutional neural network and a capsule network with dynamic routing to extract spatial features and angular part-whole relationships from LF images.
2D sub-aperture images are rendered from LF data for feature extraction.

Main Results:

CapsField demonstrates superior performance in both face recognition and expression recognition tasks compared to state-of-the-art methods.
The proposed "in the wild" LF face dataset and annotated expression subset facilitated comprehensive performance assessment.

Conclusions:

CapsField offers a significant advancement for face and expression recognition in unconstrained environments using light field technology.
The developed datasets are valuable resources for future research in LF-based facial analysis.