Abstract:
Face recognition technology has gained significant attention for its ability to identify individuals based on their facial features. This technology has various applications in computer vision, including face detection, expression detection, and video surveillance. In this study, we propose a face recognition-based attendance system using three different machine learning algorithms: Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). The system utilizes Deep Neural Networks (DNN) for face detection and employs PCA and LDA feature extraction methods for SVM and MLP approaches. In the CNN-based approach, the face images are directly fed into the CNN module as a feature vector. The proposed system achieves high recognition accuracy, with testing accuracies of approximately 98% for SVM, MLP, and CNN on a self-generated database.
If you're searching for the best final year engineering project in computer science, look no further than our IEEE Project Centers in Bangalore. We offer complete support and components for your project, including:
Comprehensive documentation support to ensure a well-documented project.
Implementation of working hardware and software in your environment.
Customized classes and guidance tailored to your project requirements.
By choosing our project center in Bangalore, you can ensure that your final year engineering project on face recognition-based attendance system using machine learning algorithms will be exceptional. Contact us now to avail our services and receive expert guidance throughout your project development process.