Facial Emotion Recognition Using Deep Learning Approach
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Date
2025-06-15
Journal Title
Journal ISSN
Volume Title
Publisher
Mohamed Boudiaf University of M'sila
Abstract
This thesis aims to design and implement a system for recognizing human emotions based on facial
expressions, using artificial intelligence techniques, particularly Convolutional Neural Networks (CNN).
Emotion recognition is an emerging and important field within affective computing, with broad
applications in mental health, education, marketing, surveillance systems, and human-computer
interaction.
In this work, a facial image dataset containing various emotions (such as anger, happiness, sadness,
surprise...) was used. The images underwent preprocessing steps such as grayscale conversion and
resizing. The CNN model was then trained using environments like JupyterLab and Google Colab, with
tools such as TensorFlow and Keras used for model design and evaluation.
The system consisted of the following main stages:
Face Detection
Feature Extraction
Emotion Classification
The results showed good accuracy in emotion recognition, confirming the effectiveness of the proposed
model. A simple application interface was also developed to test the model on both live and stored
images, bringing the project closer to real-world applications.
Despite the promising results, some challenges remain, such as lighting conditions, facial angle
variations, and similarities between emotional expressions. This opens the door for more advanced
future work, such as integrating multiple modalities (voice, text, facial expression) or adopting more
powerful models trained on more diverse datasets.
Description
Keywords
Artificial intelligence, deep learning, convolutional neural networks (CNNs), emotion recognition, face recognition, sentiment analysis