Face Recognition Using Convolutional Neural Network

Abstract

This study proposes a Real-time Face Recognition leveraging deep learning techniques to address the computational constraints and processing speed requirements of such applications. This model focuses on efficient feature extraction and comparison mechanisms to enable rapid and accurate face verification in real-time scenarios. By incorporating techniques like transfer learning and model compression, the proposed network achieves a balance between accuracy and computational efficiency. The utilization of deep learning enables the network to automatically learn discriminative features from facial images, enhancing the overall verification performance. Additionally, the lightweight design of the network ensures minimal resource consumption, making it suitable for deployment on low-power devices without compromising on performance. Experimental results demonstrate the effectiveness of the proposed model in achieving real-time face verification while maintaining high accuracy levels. Overall, the model presented in this study offers a promising solution for real-time face verification applications where speed and efficiency are crucial factors, showcasing the potential of deep learning techniques in enhancing biometric authentication systems.