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Facial Emotion Recognition

3260 papers • 126 benchmarks • 313 datasets

Emotion Recognition from facial images

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in facial-emotion-recognition-1

Trend

Dataset

Best Model

Actions

RAVDESS

Libraries

Use these libraries to find facial-emotion-recognition-1 models and implementations

Datasets

RAVDESS

CANDOR Corpus

Thermal Face Database

MH-FED

Subtasks

No subtasks available.

Most implemented papers

Facial Emotion Recognition Using Transfer Learning in the Deep CNN

M. Akhand, Shuvendu Roy, N. Siddique, Abdus Samad Kamal, T. Shimamura•Mon Apr 26 2021

A novel pipeline strategy is introduced, where the training of the dense layer(s) is followed by tuning each of the pre-trained DCNN blocks successively that has led to gradual improvement of the accuracy of FER to a higher level.

259

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Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

Facial Emotion Recognition: State of the Art Performance on FER2013

Yousif Khaireddin, Z. Chen•Fri May 07 2021

This work adopts the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods to achieve the highest single-network classification accuracy on the FER2013 dataset.

200 0

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Facial Emotion Recognition with Noisy Multi-task Annotations

L. Gool, D. Paudel, Zhiwu Huang, Siwei Zhang•Sun Oct 18 2020

This work introduces a new problem of facial emotion recognition with noisy multi-task annotations, and suggests a formulation from the point of joint distribution match view, which aims at learning more reliable correlations among raw facial images and multi- task labels, resulting in the reduction of noise influence.

11 0

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Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition

A. Vulpe-Grigorași, O. Grigore•Wed Mar 24 2021

This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition. The optimal hyperparameters of the network were determined by generating and training models based on Random Search algorithm applied on a search space defined by discrete values of hyperparameters. The best model resulted was trained and evaluated using FER2013 database, obtaining an accuracy of 72.16%.

34 0

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Facial Emotion Recognition: A multi-task approach using deep learning

Aakash Saroop, Pathik Ghugare, Sashank Mathamsetty, Vaibhav Vasani•Wed Oct 27 2021

This paper proposes a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion, and shows that this approach is significantly better than the current State of the art algorithms for this task.

6 0

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Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism

J. M. M. Torres, Sara E. Medina-DeVilliers, T. Clarkson, M. Lerner, G. Riccardi•Wed Nov 24 2021

This study is the first to bridge previous neuroscience and ASD research findings to feature-relevance calculation for EEG-based emotion recognition with CNN in typically-development (TD) and in ASD individuals.

54 0

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A Proposal for Multimodal Emotion Recognition Using Aural Transformers and Action Units on RAVDESS Dataset

Cristina Luna-Jiménez, Ricardo Kleinlein, F. Fernández-Martínez, D. Griol, Z. Callejas, J. M. Montero•Wed Dec 29 2021

Results demonstrated that these modalities carried relevant information to detect users’ emotional state and their combination allowed to improve the final system performance.

98 0

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Self-attention fusion for audiovisual emotion recognition with incomplete data

M. Gabbouj, Alexandros Iosifidis, K. Chumachenko•Tue Jan 25 2022

This paper proposes an architecture capable of learning from raw data and describes three variants of it with distinct modality fusion mechanisms that improves performance drastically under the absence/noisy representations of one modality, and improves the performance in a standard ideal setting, outperforming the competing methods.

62 0

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Facial Emotion Recognition | State-of-the-Art