Smart Artificial Intelligence Based Face Aging Recognition System Using Modified Hybrid Learning Strategy

This paper presents a pioneering approach to face aging recognition, harnessing the synergy of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) within a Modified Hybrid Learning Strategy (MHLS). Accurate face aging recognition is pivotal across various domains, including law enforcement, entertainment, and healthcare. Traditional methods often struggle with accurately predicting facial changes over time due to challenges such as varying lighting conditions, expressions, and occlusions. The proposed system addresses these challenges by amalgamating GANs to generate lifelike aging progression images and CNNs to extract discriminative features for age classification. The Modified Hybrid Learning Strategy amalgamates supervised and unsupervised learning approaches, enabling the model to glean insights from both labeled and unlabeled data. The GANs module comprises a generator network trained to produce realistic aged face images from input face images and a discriminator network trained to differentiate between real aged images and generated ones. This adversarial training encourages the generator to produce high-fidelity aged face images that closely resemble real ones. The MHLS framework facilitates the concurrent training of the GANs and CNN components, fostering mutual reinforcement and enhancing overall performance. Furthermore, the integration of unsupervised learning via GANs enables the model to learn from extensive amounts of unlabeled data, bolstering its ability to generalize to unseen faces and aging patterns. Experimental results on benchmark datasets showcase the efficacy of the proposed approach, boasting an impressive accuracy rate of 98%.