ArticleSensors (Basel, Switzerland)2019
Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- A multimodal spatiotemporal convolutional network with attention mechanism for athlete anxiety behavior recognition.Scientific reports · 2026Article
- Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory.PloS one · 2025Article
- Facial emotion recognition using deep quantum and advanced transfer learning mechanism.Frontiers in computational neuroscience · 2024Article
- Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions.Sensors (Basel, Switzerland) · 2023Article
- Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP.Sensors (Basel, Switzerland) · 2023Article
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- Emotional Speech Recognition Method Based on Word Transcription.Sensors (Basel, Switzerland) · 2022Article
- Emotion and Stress Recognition Related Sensors and Machine Learning Technologies.Sensors (Basel, Switzerland) · 2021Article
- Facial geometric feature extraction based emotional expression classification using machine learning algorithms.PloS one · 2021Article
- Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care.Sensors (Basel, Switzerland) · 2020Article
- eXnet: An Efficient Approach for EmotionRecognition in the Wild.Sensors (Basel, Switzerland) · 2020Article
- A Case Study of Facial Emotion Classification Using Affdex.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human-machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.