Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model

Abstract

Face age progression aims to change an individual’s face from a provided face image to forecast how that image will look in the future. Face aging is gaining much attention in today’s environment, which needs better security and a touchless unique identification mechanism. Researchers are focused on creating face processing algorithms to address the difficulty of producing realistic aged faces for smart system applications over the earlier decades. In the literature, the two basic needs of face age progression, aging accuracy, and identity preservation are not thoroughly addressed. According to the extraordinary gains in image synthesis made by deep generative methods and their significant influence on a wide variety of practical applications such as identifying missing persons using entertainment, childhood images, and so on, face age progression/regression has reawakened attention. The majority of present techniques concentrate on face age progression and is beneficial and productive in learning the transition across age groups utilizing paired data, i.e., face images of the similar individual at various ages. Through the motivation of the important success attained by Generative Adversarial Networks (GANs), this paper uses tactics to implement the improved Cycle GAN-based intelligent face age progression model. Initially, the standard datasets for the face progression are gathered, and the face is detected using the Viola-Jones object detection algorithm. Then, the pre-processing of the facial image is performed by median filtering and contrast enhancement techniques. Once the image is pre-processed, the Hyperparameter Tuning-Cycle GAN (HT-Cycle GAN) is adopted for face age progression. As an improvement, the hyperparameters of the Cycle GAN are optimized or tuned by the modified Galactic Swarm Optimization (GSO), known as Best Fitness-based Galactic Swarm Optimization (BF-GSO). From the evaluation of statistical analysis, the similarity score of BF-GSO-HT-CycleGAN is 0.80%, 3.33%, and 2.86% higher than cGAN, CycleGAN, and Dubbed FaceGAN, respectively. Here, the Dubbed FaceGAN is the 2nd greatest network. Furthermore, compared to traditional models utilizing distinct standard datasets, the experimental findings show that the suggested technique attains efficiency, accuracy, and flexibility.