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yashasvini121 / predictive-calc

An interactive web application developed with Streamlit, designed for making predictions using various machine learning models. The app dynamically generates forms and pages from JSON configuration files. ⭐ If you found this helpful, consider starring the repo!
https://predictive-calc.streamlit.app/
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Emotion Recognition Using CNN and OpenCV #31

Open ChitteshKumar opened 2 days ago

ChitteshKumar commented 2 days ago

Problem Description: The objective is to develop a model that can automatically recognize human emotions from facial expressions in images. This problem is crucial in areas like human-computer interaction, mental health analysis, and security. Accurately identifying emotions such as happiness, sadness, anger, or surprise can enhance applications in video surveillance, virtual assistants, and customer service.

Model Description: I propose using a Convolutional Neural Network (CNN) for emotion recognition. CNNs are highly effective for image processing tasks due to their ability to detect spatial hierarchies and patterns within images. By leveraging OpenCV for facial recognition, we can preprocess input images to detect and isolate faces before feeding them into the CNN model.

For this task, we will use a pre-labeled emotion recognition dataset from Kaggle, which contains images tagged with various emotion labels. The CNN will be trained to classify these emotions from facial images. In particular, the network will focus on key facial features like eyes, mouth, and eyebrows, which are critical in conveying emotions.

If necessary, transfer learning can be utilized by employing pre-trained models like VGG16 or ResNet or I have my own model for transfer learning, which can help improve accuracy and speed up training time.

Estimated Time for Completion: I estimate that it will take around 1-2 weeks to fully develop, train, and test the model. This includes time for data preprocessing, model design, hyperparameter tuning, and final deployment. Factors such as data augmentation and model optimization may affect this timeline.

Expected Outcome: Once implemented, the model is expected to:

This solution will also lay the foundation for more advanced features, such as combining emotion recognition with other biometric factors for deeper insights.

Additional Context: The Kaggle dataset will provide a solid foundation for training the model. It would also be useful to explore OpenCV's facial detection capabilities to preprocess the input images effectively. Data augmentation (such as rotation, scaling, and flipping) will be applied to enhance model robustness.

@yashasvini121, I would be excited to work on this issue under gssoc24-extd, and I'm confident this project will add significant value to emotion recognition systems.

Thank you for considering this proposal.

yashasvini121 commented 2 days ago

Sure, @ChitteshKumar, go ahead. However, please try to complete it within a week, or provide a progress update for sure.