| Title | Facial Recognition Technology Using Deep Neural Networks: Performance Evaluation and Ethical Challenges |
| Research Area | Facial Recognition |
| Abstract | Facial Recognition Technology (FRT) has gained widespread adoption in security systems, surveillance, access control, and identity verification due to advancements in deep learning and computer vision. Deep Neural Networks (DNNs), particularly Convolutional Neural Networks (CNNs), have significantly improved the accuracy and robustness of facial recognition systems. This paper presents a comprehensive study of deep learning-based facial recognition techniques, focusing on system architecture, performance evaluation, and ethical considerations. The proposed framework evaluates popular deep neural network models for face detection and recognition using benchmark datasets. Experimental results indicate that deep learning models achieve recognition accuracy above 95% under controlled conditions. However, the study also highlights concerns related to privacy, bias, and misuse of facial data. The paper concludes by emphasizing the need for responsible deployment and regulatory oversight of facial recognition technologies. |
| Keywords | Facial Recognition, Deep Learning, Convolutional Neural Networks, Biometrics, Ethics, Privacy |
| Paper Status | Published |
| Volume | 1 |
| Issue | 6 |
| Published On | 23/11/2025 |
| Published File |
IJSRTD_4867.pdf
|