The "Face Recognition Using PCA and K-NN Algorithms" project is a Python-based application that utilizes Principal Component Analysis (PCA) and K-Nearest Neighbors (K-NN) algorithms to recognize faces from images. The project aims to provide a reliable and efficient solution for face recognition tasks, such as identifying individuals from a database of images or detecting known faces in real-time video streams.
This is implemented in Python and utilizes popular libraries such as OpenCV, NumPy, and scikit-learn for image processing, matrix operations, and machine learning tasks
- Captures images of people for training, and these images are automatically converted to grayscale with a size of 224x224 pixels.
- Manually, you can train a dataset and save the data to files (pca.pkl, trainDataS.pkl, trainDataS.csv.
- Performs real-time face recognition using K-nearest neighbors (KNN) classifier with PCA for dimensionality reduction on camera frames
- Python 3 installed on your system
To get started with the face recognition project, follow these steps:
- Clone the GitHub repository:
git clone https://github.com/hexa2525/face_recognition-pca-knn.git - Navigate to the project directory:
cd face_recognition-pca-knn - Install the required dependencies using pip:
python3 -m pip install -r requirements.txt
-
Navigate to the project directory:
cd face_recognition-pca-knn -
Simply run
python3 face_reco.py
- customtkinter==5.1.2
- pandas==1.5.3
- scikit-learn: 0.24.2 or higher
- numpy: 1.19.5 or higher
- OpenCV: 4.5.2 or higher