HOUSE SECURITY THROUGH FACE RECOGNITION USING OPENCV

Abstract

Face Recognition is a widely researched and applied field with numerous practical applications, from
security systems to personalized user experiences. This project introduces a Face Recognition system that
harnesses the power of machine learning and OpenCV, a versatile computer vision library, to accurately identify
individuals from facial images. The project employs a combination of feature extraction, machine learning
algorithms, and facial landmark detection techniques to create a robust face recognition model.
Machine learning algorithms like Convolutional Neural Networks (CNNs), Support Vector Machines
(SVMs) or k-Nearest Neighbors (k-NN) are trained on the extracted features to create a face recognition model.
The model's accuracy is evaluated using various metrics and benchmark datasets, ensuring its effectiveness
across different lighting conditions, poses, and facial expressions. The project also supports real-time face
recognition from video streams or camera feeds. The implementation addresses privacy concerns by focusing
solely on facial features for recognition, rather than storing or sharing personal data.