Hybrid deep neural networks for face emotion recognition

doi:10.1016/j.patrec.2018.04.010

Paper

Hybrid deep neural networks for face emotion recognition

Published Nov 1, 2018 · Neha Jain, Shishir Kumar, Amit Kumar

Pattern Recognit. Lett.

Q1 SJR score

223

Citations

18

Influential Citations

Full text

Semantic Scholar

Abstract

Abstract removed due to Elsevier request; this does not indicate any issues with the research. Click the full text link above to read the abstract and view the original source.

Highly Cited

Study Snapshot

Key takeawayThe Hybrid Convolution-Recurrent Neural Network method improves Facial Expression Recognition accuracy and is promising for real-time applications.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Sign up to use Study Snapshot

Consensus is limited without an account. Create an account or sign in to get more searches and use the Study Snapshot.

Create an account

Paper

Hybrid deep neural networks for face emotion recognition

Sign up to use Study Snapshot

References

Citations

Transparent Machine Vision Techniques For Facial Emotion Detection

Machine vision techniques, such as Vision Transformers and CNN, can effectively detect facial expressions in practical settings, with Lime being the best explainability technique.

Action Unit Analysis for Monitoring Drivers’ Emotional States

This study presents an innovative framework that adapts machine learning models to real-time monitoring of drivers' emotional states, achieving enhanced precision and reducing computational load in intelligent automotive systems.

Exploring the Impact of Convolutional Neural Networks on Facial Emotion Detection and Recognition

Convolutional neural networks (CNNs) have significantly advanced facial emotion detection and recognition, leading to highly accurate results in various industries and emotional AI applications.

Facial Emotion Detection: A Comprehensive Survey

Deep learning models like Convolutional Neural Networks (CNN) show better accuracy in identifying facial emotions compared to traditional Machine Learning Algorithms, benefiting various professional fields.

Application of a Voting-Based Ensemble Method for Recognizing Seven Basic Emotions in Real-Time Webcam Video Images

A custom CNN model and a voting-based ensemble method can accurately recognize seven basic emotions in real-time webcam video images, with a 95% accuracy rate on the FER2013 dataset.

Face Emotion Recognition Based on Machine Learning: A Review

Machine learning techniques like k-nearest neighbor, naive Bayesian, support vector machine, and random forest can effectively identify emotions in faces, but future improvements are needed to overcome limitations in facial expressions, body language, and posture.

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Deep facial expression recognition (FER) has evolved from traditional methods to deep learning, addressing challenges like overfitting and unrelated variations.