https://github.com/alkaifaftab000/face-emotion-detection

Detecting emotions with AI: from happy smiles to sad sighs, one face at a time!😊
https://github.com/alkaifaftab000/face-emotion-detection

deep deep-learning emotion-detection image-processing keras neural-network tensorflow

Last synced: about 2 months ago
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Detecting emotions with AI: from happy smiles to sad sighs, one face at a time!😊

• Host: GitHub
• URL: https://github.com/alkaifaftab000/face-emotion-detection
• Owner: alkaifaftab000
• Created: 2024-11-25T13:46:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-17T13:46:49.000Z (over 1 year ago)
• Last Synced: 2025-07-15T04:09:37.431Z (11 months ago)
• Topics: deep, deep-learning, emotion-detection, image-processing, keras, neural-network, tensorflow
• Language: HTML
• Homepage: https://face-emotion-detection-o5wy.onrender.com/
• Size: 52.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 😊 Face Emotion Detection Project

This project aims to detect emotions (happy or sad) from images using deep learning techniques. It's perfect for exploring the intersection of technology and human emotions.

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## 🎯 Workflow

1. **Data Collection**:  

   - Downloaded 1,000 images featuring sad and happy faces, including babies, children, adults, and elderly people.  

   - Detect faces directly using a camera or by adding your own images.

2. **Data Preprocessing**:  

   - Removed irrelevant images (like dog pictures 🐕) using `cv2` and `imghdr`.  

   - Normalized pixel values for consistency in processing.

3. **Building the Model**:  

   - Employed **Keras** with **TensorFlow** backend to build a convolutional neural network (CNN):  

      - **Layers**:  

        - Conv2D  

        - MaxPooling2D  

        - Dense  

        - Flatten  

      - Small filters in Conv2D to extract intricate features.  

   - The model outputs a score:  

      - Close to `0`: Person is sad 😢  

      - Close to `1`: Person is happy 😃

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## 🚀 Learning Outcomes

1. Understanding the preprocessing techniques to handle image-based data effectively.  

2. Building a deep learning model with CNNs to extract features and classify emotions.

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## UI 

![{C82E27E4-26F4-4A42-BAE1-F45701B187C0}](https://github.com/user-attachments/assets/91975f13-2ceb-4b66-ac0d-2e22e4e404f9)

![{6C127782-44F0-4B2E-965B-620450302770}](https://github.com/user-attachments/assets/fc4ff745-6dc2-4114-a212-27cfe79360d8)

## Result

![{F9B3092B-A6E7-4319-9D27-5071B30384E7}](https://github.com/user-attachments/assets/7531621b-b997-4701-8396-2c3a7aba85f1)

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## 🔧 Plugins Used

- `flask==3.0.2`  

- `tensorflow==2.18.0`  

- `pillow==10.2.0`  

- `numpy==1.26.4`  

- `gunicorn==21.2.0`  

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