Live Class Monitoring System by Face Emotion Recognition

Live Class Monitoring System by Face Emotion Recognition

Sayali Chavan1, Shraddha Sanap2, Aparna Bharti3 Aarti Phad4

1Computer, Matoshri College of engineering and research centre

2Computer, Matoshri College of engineering and research centre

3Computer, Matoshri College of engineering and research centre

4Computer, Matoshri College of engineering and research centre

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Abstract -The sentiments influence information processing, attitude formation, and decision making to a great extent in real-world scenarios. Several recent efforts have been published about FER or facial expression recognition, however, due to the diversity of human faces and fluctuations in pictures, reliable and robust FER systems remain a challenge. Till date, every study and work has proposed either a single network or an ensemble model. The accuracy of ensemble models is higher, but they were associated with many models and datasets and a few tweaked datasets to improve the accuracy, increasing the computing complexity. While the majority of research in this field focuses on improving accuracy, this study utilizes the proposed model to a real-world scenario in which a person’s face contains a mix of emotions, and a single-label sentiment can be highly noisy in such situations. In view of this scenario, we developed and tested 15-20 models and methods. In this paper, we propose a single standalone-based CNN model with its implementation on a real-time Intelligent System for Sentiment Recognition, which validates accuracy through transfer learning and performs tasks such as face detection, sentiment classification, and providing a live list of probabilistic labels in Real-time from a webcam feed in one blended step.

Key Words- Facial Emotion Recognition, Deep Convolutional Neural Network, Classification, Adam.