Face Detection with MTCNN Using Densenet for Enhance Security
Mehedi Hassan et al. · Journal of Networking and Communication Systems (JNACS) · 2025
The research tackles a major security system vulnerability: the ability of current face detection systems to identify changes in circumstances and spoofing attacks.The objective is to create a system that is resistant to security threats and can detect faces reliably under a range of circumstances and scales.The study suggests integrating Dense Convolutional Networks (Dense Net) with Multi-Task Cascaded Convolutional Networks (MTCNN) to do this.Dense Net is a deep learning model that takes advantage of its densely linked layers to improve on MTCNN, a widely used face detection technique.The proposed system improves the performance of face detection, making it more robust and secure.The effectiveness of the combined model is validated through rigorous experimentation and evaluation of standard datasets.The results demonstrate the potential of Dense Net in improving the robustness and security of face detection systems, paving the way for more secure applications in areas such as surveillance, biometric authentication, and social media.