FACIAL EXPRESSION RECOGNITION: BRIDGING TECHNOLOGY AND EMOTION
Author(s):
Mathala Pavankalyan , Kudaravalli lohitha, Panam Amariah, Petta Pavansivasai, DR.D.SUNEETHA
Keywords:
Facial Expression Recognition, Convolutional Neural Networks (CNNs), TensorFlow, Keras, OpenCV, Python, Deep Learning, Image Processing, Real-time Analysis, Human-Computer Interaction, Emotion Detection, Virtual Communication, Mental Health Assessment, Computer Vision, Machine Learning, Human Emotion Analysis.
Abstract
Facial expression recognition plays a pivotal role in bridging human interaction with technology, enabling seamless communication between individuals and machines. In this project, we employ a sophisticated approach utilizing Convolutional Neural Networks (CNNs) for real-time facial expression recognition. Leveraging the power of TensorFlow and Keras frameworks, our system is designed to train, evaluate, and deploy CNN models with efficiency and accuracy. OpenCV serves as the backbone for processing webcam frames, providing essential functionalities for image manipulation and display. Python, with its simplicity and vast ecosystem of libraries, forms the foundation of our implementation, facilitating seamless integration of various components. While exact accuracy figures depend on factors such as dataset and model architecture, our project offers a robust starting point for facial expression recognition tasks. We emphasize the importance of rigorous evaluation using standard metrics such as accuracy, precision, recall, and F1 score, along with experimentation with different datasets and model configurations to enhance performance. In conclusion, our project showcases the effective utilization of CNNs, TensorFlow, Keras, OpenCV, and Python in developing a facial expression recognition system. By advancing the understanding of facial expressions, we aim to enhance human-machine interaction and pave the way for more intuitive and empathetic technology interfaces.
Article Details
Unique Paper ID: 162345

Publication Volume & Issue: Volume 10, Issue 9

Page(s): 271 - 276
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