Published on Sat Jan 18 2020

Deep Metric Structured Learning For Facial Expression Recognition

Pedro D. Marrero Fernandez, Tsang Ing Ren, Tsang Ing Jyh, Fidel A. Guerrero Peña, Alexandre Cunha

We propose a deep metric learning model to create embedded sub-spaces with a well defined structure. We also propose a new semi-supervised method to create sub-classes. We illustrate our methods on the facial expression recognition problem.

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Abstract

We propose a deep metric learning model to create embedded sub-spaces with a well defined structure. A new loss function that imposes Gaussian structures on the output space is introduced to create these sub-spaces thus shaping the distribution of the data. Having a mixture of Gaussians solution space is advantageous given its simplified and well established structure. It allows fast discovering of classes within classes and the identification of mean representatives at the centroids of individual classes. We also propose a new semi-supervised method to create sub-classes. We illustrate our methods on the facial expression recognition problem and validate results on the FER+, AffectNet, Extended Cohn-Kanade (CK+), BU-3DFE, and JAFFE datasets. We experimentally demonstrate that the learned embedding can be successfully used for various applications including expression retrieval and emotion recognition.

Tue Nov 27 2018
Artificial Intelligence
A Compact Embedding for Facial Expression Similarity
Facial expressions do not always fall neatly into pre-defined semantic categories. The similarity between expressions measured in the action unit space need not correspond to how humans perceive expression similarity. We demonstrate that the learned embedding can be successfully used for various applications such as expression retrieval.
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Thu May 10 2018
Computer Vision
Deep Covariance Descriptors for Facial Expression Recognition
The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By performing the classification of the facial expressions using Gaussian kernel on SPD manifold, we show that the DCNN features are more efficient than the standard classification.
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Wed May 12 2021
Computer Vision
Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognition
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Mon Jan 11 2016
Computer Vision
Facial Expression Recognition in the Wild using Rich Deep Features
Facial Expression Recognition in the wild remains an area where much work is still needed to serve real-world applications. We fuse rich. features with domain knowledge through encoding discriminant facial patches.
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Mon Nov 16 2015
Computer Vision
Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition
Facial expression is a temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account.
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Tue Jun 15 2021
Computer Vision
Efficient Facial Expression Analysis For Dimensional Affect Recognition Using Geometric Features
The proposed approach is robust, efficient, and exhibits comparable performance to contemporary deep learning models, while requiring a fraction of the computational resources. The system learns to estimate Arousal and Valence ratings from a set of facial images.
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