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BioVision: A Secure and Accurate AI Framework for Visual Facial Expression Analysis
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/IREV9I10-1716225
Abstract
Facial Emotion Recognition (FER) is a pivotal component of Affective Computing, enabling machines to interpret human psychological states. Traditional FER systems often operate in isolation, lacking real-time interactive feedback or user engagement mechanisms. In this paper, we propose "Bio Vision," an end-to-end real-time emotion detection system integrated with a gamified and interactive web interface. The proposed system utilizes a Convolutional Neural Network (CNN) trained on facial expression datasets to classify seven universal emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise. Faces are continuously detected using Haar Cascade Classifiers via OpenCV, and predictions are served through a lightweight Flask API. The frontend architecture employs dynamic data visualization, bilingual voice feedback (English and Hindi), and emotion-responsive gamification, where game mechanics adapt dynamically to the user's emotional state. Experimental results indicate robust real-time performance with minimal latency, demonstrating the system's viability for applications in mental health monitoring, human-computer interaction, and digital well-being.
Keywords
Facial Emotion Recognition, Convolutional Neural Networks (CNN), Affective Computing, Gamification, Human-Computer Interaction (HCI), Computer Vision, OpenCV.
References
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How to cite this paper
@article{1716225,
author = {Avnish Kumar, Keshav Kumar, Tajendra Riya, Prof. (Dr.) Sanjay Pachauri},
title = {BioVision: A Secure and Accurate AI Framework for Visual Facial Expression Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1440-1445},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716225.pdf},
abstract = {Facial Emotion Recognition (FER) is a pivotal component of Affective Computing, enabling machines to interpret human psychological states. Traditional FER systems often operate in isolation, lacking real-time interactive feedback or user engagement mechanisms. In this paper, we propose "Bio Vision," an end-to-end real-time emotion detection system integrated with a gamified and interactive web interface. The proposed system utilizes a Convolutional Neural Network (CNN) trained on facial expression datasets to classify seven universal emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise. Faces are continuously detected using Haar Cascade Classifiers via OpenCV, and predictions are served through a lightweight Flask API. The frontend architecture employs dynamic data visualization, bilingual voice feedback (English and Hindi), and emotion-responsive gamification, where game mechanics adapt dynamically to the user's emotional state. Experimental results indicate robust real-time performance with minimal latency, demonstrating the system's viability for applications in mental health monitoring, human-computer interaction, and digital well-being.},
keywords = {Facial Emotion Recognition, Convolutional Neural Networks (CNN), Affective Computing, Gamification, Human-Computer Interaction (HCI), Computer Vision, OpenCV.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716225}
}