face-recognition

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👤 Face Recognition using KNN & OpenCV

This project is a real-time face recognition system that uses the K-Nearest Neighbors (KNN) algorithm and OpenCV. It detects faces from a webcam feed, classifies them based on stored training data, and displays the predicted name in real time.

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📌 Features

• ✅ Face Detection using OpenCV’s Haar Cascade.
• ✅ Face Recognition with KNN implemented from scratch (no scikit-learn).
• ✅ Real-time Webcam Prediction with bounding boxes and labels.
• ✅ Easily Extendable Dataset – add new people by storing their face data as .npy files.

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🛠️ Tech Stack

• Python 3.x
• NumPy → handling dataset & distance calculations.
• OpenCV → face detection, image preprocessing, and real-time video capture.
• KNN (custom implementation) → classification of faces.

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🚀 How it Works

1. Dataset Creation

   • Each person’s face images are stored as a .npy file.
   • Example: AnvikaNegi.npy → contains all face images of Anvika.
   • The dataset loader maps numeric IDs → names.

2. Training (Data Preparation)

   • .npy files are loaded and concatenated into:

     • X → feature matrix of flattened face images.
     • y → labels (numeric IDs for each person).

3. KNN Algorithm

   • For a new input face:

     • Compute distance from all training images.
     • Pick the top k nearest neighbors.
     • Assign the label by majority voting.

4. Real-time Prediction

   • Webcam captures a frame.
   • OpenCV detects face regions.
   • Each face is cropped, resized, and passed to KNN.
   • The predicted name is displayed on screen.

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# Face Recognition using KNN

This project implements a simple real-time face recognition system using **OpenCV** and **K-Nearest Neighbors (KNN)**.

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## 🔹 Steps to Run the Project

### 1. Clone the Repository
```bash
git clone https://github.com/GauravSingh0248/face-recognition.git
cd face-recognition/FaceRecognition_KNN

2. Create Virtual Environment (Recommended)

python -m venv venv
# Activate the virtual environment
# Linux/Mac
source venv/bin/activate
# Windows
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

If you don’t have a requirements.txt yet, create one with:

pip freeze > requirements.txt

Likely required packages:

• numpy
• opencv-python
• matplotlib

4. Collect Training Data (Faces)

Run the script to capture face data:

python face_data_collect.py

• A webcam window will open.
• Enter your name/ID when prompted.
• Press q to stop capturing.
• Data will be saved in the data/ folder.

5. Train & Test Recognition with KNN

Run the recognition script:

python face_recognition_knn.py

• The webcam will open.
• It will recognize faces in real-time using the KNN classifier.
• Press q to exit.

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🔹 Notes

• Ensure your webcam works properly.
• You can add multiple users by running face_data_collect.py for each user.
• The .npy files in the data/ folder are your training datasets.
• Recognition is real-time using OpenCV + KNN.

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## 📸 Example Output

* **Dataset loading**

AnvikaNegi.npy (34, 30000) Gaurav.npy (55, 30000) (89, 30000) (89, 1) {0: 'AnvikaNegi', 1: 'Gaurav'}

* **Live Webcam Prediction**
  Bounding box with predicted name:

[Face Detected] → Predicted: Gaurav

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## 📌 Future Improvements

* 🔹 Improve face detection using DNN or MTCNN.
* 🔹 Add support for more robust classifiers (SVM, CNN).
* 🔹 Store embeddings instead of raw pixel intensities for better accuracy.

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## 👨‍💻 Author

Developed by **Gaurav Singh** ✨
Contact -- [email protected]
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