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https://github.com/xgagandeep/face-recognition-from-scratch-with-knn

This project implements a face recognition system using the k-Nearest Neighbors (KNN) algorithm from scratch.
https://github.com/xgagandeep/face-recognition-from-scratch-with-knn

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This project implements a face recognition system using the k-Nearest Neighbors (KNN) algorithm from scratch.

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# Face Recognition from Scratch with KNN



**Repository:** [xgagandeep/Face-Recognition-from-Scratch-with-knn](https://github.com/xgagandeep/Face-Recognition-from-Scratch-with-knn)  

**Date:** 2020  

**Language:** Python  

**Libraries:** OpenCV, NumPy



## Description



This project implements a face recognition system using the k-Nearest Neighbors (KNN) algorithm from scratch. The notebook demonstrates how to capture real-time video feed, detect faces, and recognize them based on previously stored face data.



## Features



- **Real-Time Face Detection:** Uses OpenCV's Haar Cascade Classifier to detect faces in a video feed.

- **Face Recognition:** Implements KNN to recognize faces by comparing them with a dataset of known faces.

- **Real-Time Display:** Displays the video feed with bounding boxes and recognized names overlaid on detected faces.



## Files



- **`face recognition knn.ipynb`:** Jupyter Notebook containing the implementation of the face recognition system using KNN.



## Installation



To run this project, you need Python and the required libraries installed. Follow these steps:



1. **Clone the repository:**



   ```bash

   git clone https://github.com/xgagandeep/Face-Recognition-from-Scratch-with-knn.git

   ```



2. **Navigate to the project directory:**



   ```bash

   cd Face-Recognition-from-Scratch-with-knn

   ```



3. **Install the required libraries:**



   ```bash

   pip install numpy opencv-python

   ```



4. **Download the Haar Cascade XML file:**



   The Haar Cascade XML file (`haarcascade_frontalface_alt.xml`) is used for face detection. You can download it from the [OpenCV GitHub repository](https://github.com/opencv/opencv/tree/master/data/haarcascades) and place it in the appropriate directory.



5. **Prepare the face data:**



   Ensure you have a directory (`data`) with face data files saved in `.npy` format. These files should contain face images in numpy array format, where each file corresponds to a different person.



6. **Run the Jupyter Notebook:**



   ```bash

   jupyter notebook face\ recognition\ knn.ipynb

   ```



## Usage



1. **Face Data Preparation:**

   - Load existing face data from `.npy` files.

   - Combine face data and labels into a single dataset for training.



2. **Real-Time Face Recognition:**

   - Capture video feed from the camera.

   - Detect faces using Haar Cascade.

   - Extract face sections, resize them, and flatten for KNN classification.

   - Display the recognized face name on the video feed.



3. **Exit the Application:**

   - Press 'q' to quit the video feed and close the application.



## Functions



- **`dist(X1, X2)`**: Computes the Euclidean distance between two vectors.

- **`knn(X, Y, Query, k=5)`**: Applies KNN to classify a query face based on a dataset of known faces.

- **`cv2.VideoCapture("0")`**: Captures video feed from the default camera.

- **`cv2.CascadeClassifier()`**: Loads the Haar Cascade classifier for face detection.

- **`cv2.imshow()`**: Displays the video feed with detected and recognized faces.



## Example



The notebook captures real-time video, detects faces, and recognizes them based on pre-trained face data. Make sure to have your `data` directory set up with face images and update the paths accordingly.



## Contribution



Feel free to contribute to this project by submitting issues or pull requests. For any questions or feedback, please open an issue on the GitHub repository.