Emotion Sense: A Deep Learning Facial Emotion Recognition System for Real-Time Application using AI
Fuente:
Zenodo
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Recurso digital |
| Pubblicazione: |
Zenodo
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901371237695488 |
|---|---|
| author | Pradumnya P Dhonde Pankaj M Kotwal Tarun V Kumar Chetan S Karpe Dr. Sivaram Ponnusamy Dr. Umesh Pawar |
| author_facet | Pradumnya P Dhonde Pankaj M Kotwal Tarun V Kumar Chetan S Karpe Dr. Sivaram Ponnusamy Dr. Umesh Pawar |
| contents | Abstract- Recognizing emotions is very important for connecting human emotions with artificial intelligence. This study introduces Emotion Sense, a sophisticated real-time facial emotion recognition system utilizing deep learning and explainable AI (XAI). The suggested system uses a better MobileNetV3 architecture along with Coordinate Attention (CA) and Grad-CAM visualization to get high accuracy and make the results easy to understand. The model recognizes seven fundamental human emotions: happiness, sadness, anger, surprise, fear, disgust, and neutrality. The FER-2013 data set. Emotion Sense solves two big problems that traditional CNN-based models have by combining real-time performance with explainability. This makes it both accurate and clear. The experimental results show that it is 90.2% accurate and runs smoothly at 25 frames per second on CPU devices. This shows that it is useful for real-world applications like healthcare, education, and human-computer interaction. This research is unique because it uses a hybrid design that balances speed, accuracy, and interpretability while staying strong in different real-world situations. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20036148 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Emotion Sense: A Deep Learning Facial Emotion Recognition System for Real-Time Application using AI Pradumnya P Dhonde Pankaj M Kotwal Tarun V Kumar Chetan S Karpe Dr. Sivaram Ponnusamy Dr. Umesh Pawar Artificial Intelligence Deep Learning Facial Emotion Recognition Explainable Al Coordinate Attention Grad- CAM Edge AI Real-Time Detection Abstract- Recognizing emotions is very important for connecting human emotions with artificial intelligence. This study introduces Emotion Sense, a sophisticated real-time facial emotion recognition system utilizing deep learning and explainable AI (XAI). The suggested system uses a better MobileNetV3 architecture along with Coordinate Attention (CA) and Grad-CAM visualization to get high accuracy and make the results easy to understand. The model recognizes seven fundamental human emotions: happiness, sadness, anger, surprise, fear, disgust, and neutrality. The FER-2013 data set. Emotion Sense solves two big problems that traditional CNN-based models have by combining real-time performance with explainability. This makes it both accurate and clear. The experimental results show that it is 90.2% accurate and runs smoothly at 25 frames per second on CPU devices. This shows that it is useful for real-world applications like healthcare, education, and human-computer interaction. This research is unique because it uses a hybrid design that balances speed, accuracy, and interpretability while staying strong in different real-world situations. |
| title | Emotion Sense: A Deep Learning Facial Emotion Recognition System for Real-Time Application using AI |
| topic | Artificial Intelligence Deep Learning Facial Emotion Recognition Explainable Al Coordinate Attention Grad- CAM Edge AI Real-Time Detection |
| url | https://doi.org/10.5281/zenodo.20036148 |