Enhanced face age progression and regression model using hyper-parameter tuning-large scale GAN by hybrid heuristic improvement

The main challenge is to automate the model for aged or de-aged face generation. However, there are certain limitations on accuracy for age estimation and identity preservation. To achieve this, a new face age progression and regression is proposed by Hyper-parameter Tuning-Large Scale Generative Adversarial Network (HT-Large Scale GAN) with Pollination Rate-based Sunflower Dolphin Swarm Optimization (PR-SDSO). The input images are collected and fed into the object detection model, where the viola Jones algorithm is utilized. Here, the pre-processing is done by median filtering and contrast enhancement. The face age progression and regression are accomplished by novel HT-Large Scale GAN, where the hyperparameters are optimized by a new algorithm of PR-SDSO. Throughout the result analysis, the proposed model ensures that it provides the appropriate synthesized images for both the progression and regression phases and acquires less error to improve the quality of the image.