Facial-Detection-KNN-SVM-GaussianNB

This project implements facial recognition using custom KNN, SVM, and Gaussian NB. It features a tailored KNN for optimized facial analysis, SVM for complex feature differentiation, and Gaussian NB for statistical predictions.

Dataset

The dataset used in this project consists of facial images represented in a tabular format. Each row corresponds to a facial image, and each column represents a pixel value or feature.

Usage

1. Load the dataset using Pandas.
2. Pre-process the dataset by normalizing each face image vector to unit length. This normalization step ensures that each image vector has a magnitude of 1, which is essential for the facial recognition process.
3. Apply machine learning techniques to train a model for recognizing faces from the dataset.

Code Structure

• Data Loading: The dataset is loaded into a DataFrame.
• Pre-processing: Normalization of the facial image vectors to unit length.
• Labeling: Assign labels to the dataset for supervised learning.
• Model Training: (Details on model training can be added based on further cells in the notebook).

License

This project is licensed under the MIT License- see the LICENSE file for details.