Face-Mask-Detection-using-Machine-Learning

Developed a face mask detection system using Support Vector Machines (SVM) and Haar-Cascade classifiers for efficient and accurate identification of mask compliance. ◦ Trained the model to recognize facial features and classify images using the Scikit-learn library, ensuring robust detection of mask presence. ◦ This project will get dynamically trained by capturing the data from the camera of the device and classifies them as with_mask and without_mask data and later it gets trained on the data and then it will go fo prediction in real-time. We can also train the model on pre-loaded dataset by making few minor changes in the code that is by replacing dynamic data input with a static dataset which was already classifies earlier