Human: Age and Gender Identification Using SFTA and Deep Learning Algorithm

Precision in ascertaining the gender and age of individuals stands as a central objective in the development of accessible and intelligent systems. This holistic approach comprises a symbiosis of cutting-edge CNN methodologies and advanced image enhancement techniques, marking a notable advancement in the realm of facial analysis. By combining the robust capabilities of CNNs with adaptive gamma correction, the model not only navigates the challenges of age determination but also stands as a testament to the continuous evolution of intelligent systems tailored for real-world applications. For validation and assessment, the proposed network undergoes rigorous training and testing procedures using the Adience (original) dataset. The integration of Segmentation-based Fractal Texture Analysis (SFTA) within the domain of deep learning, presenting a novel approach to texture analysis for image segmentation tasks. SFTA, a sophisticated technique, combines the power of fractal analysis with segmentation methods to provide a comprehensive understanding of complex textures in images. Through a series of experiments across diverse datasets, the research evaluates the performance of SFTA-based models against conventional methods. Results demonstrate that SFTA enriches the texture analysis process, leading to improved segmentation outcomes and robustness across varied image types.