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FACIAL EXPRESSION RECOGNITION: BRIDGING TECHNOLOGY AND EMOTION

  • Unique Paper ID: 162345
  • Volume: 10
  • Issue: 9
  • PageNo: 271-276
  • 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.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 9
  • PageNo: 271-276

FACIAL EXPRESSION RECOGNITION: BRIDGING TECHNOLOGY AND EMOTION

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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