ArticleSensors (Basel, Switzerland)2022
Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- A Lightweight Vision-Based Emotion Sensing Framework for Assistive Healthcare Robotics.Sensors (Basel, Switzerland) · 2026Article
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- Joint Driver State Classification Approach: Face Classification Model Development and Facial Feature Analysis Improvement.Sensors (Basel, Switzerland) · 2025Article
- Emotion estimation from video footage with LSTM.Frontiers in neurorobotics · 2025Article
- CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations.Sensors (Basel, Switzerland) · 2023Article
- Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes.Bioengineering (Basel, Switzerland) · 2023Article
- Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.Sensors (Basel, Switzerland) · 2023Article
- Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.Sensors (Basel, Switzerland) · 2023Article
- Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features.Sensors (Basel, Switzerland) · 2023Article
- A Face Detection and Standardized Mask-Wearing Recognition Algorithm.Sensors (Basel, Switzerland) · 2023Article
- A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.Sensors (Basel, Switzerland) · 2023Article
- An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.Sensors (Basel, Switzerland) · 2023Article
- Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm.PeerJ. Computer science · 2023Article
- Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The main steps of emotion recognition on masked faces include face detection by using Haar-Cascade, landmark implementation through a media-pipe face mesh model, and model training on seven emotional classes. The FER-2013 dataset was used for model training. An emotion detection model was developed for non-masked faces. Thereafter, landmarks were applied to the upper part of the face. After the detection of faces and landmark locations were extracted, we captured coordinates of emotional class landmarks and exported to a comma-separated values (csv) file. After that, model weights were transferred to the emotional classes. Finally, a landmark-based emotion recognition model for the upper facial parts was tested both on images and in real time using a web camera application. The results showed that the proposed model achieved an overall accuracy of 91.2% for seven emotional classes in the case of an image application. Image based emotion detection of the proposed model accuracy showed relatively higher results than the real-time emotion detection.
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