An Efficient Biometric Authentication Based on Face Image using Deep Generative Adversarial Network

face authentication is one of the technique to prevent the loss of personal data, however the process is challenging due to variations in the patterns of the mouth, nose, eyes, and angles. A face authentication system that leverages an enhanced Deep Generative Adversarial Network (DGAN) is being developed as a solution to the problem. The process begins with preprocessing Histogram Equalization method to enhance the image quality for generator input. GAN generator to create synthetic image using data augmentation method to identify the true face, which is helpful for identifying fake one. Then AlexNet called discriminator to extract the feature of input image and compare the real and fake images to authenticate the person. The implemented DGAN model demonstrates superior performance, achieving Precision of (97.12%), recall of (97.31%) and F1-Score of (97.01%). When compared to previous models like AutoRegressive Biometric Authentication-Oriented Face Detection (ARBAOFD), deep steganography with Celebes (DSC) and Convolutional Neural Network (CNN)