Optimizing Deep Learning for Accurate Age and Gender Classification in Real-World Applications

Accurate age and gender classification is crucial for applications in targeted marketing, content personalization, security, and demographic analysis.This study proposes an enhanced deep learning framework integrating advanced preprocessing techniques with an optimized VGG19 architecture to improve classification accuracy and computational efficiency.The preprocessing pipeline incorporates Dlib-based face alignment, histogram equalization for contrast enhancement, and gamma correction for illumination normalization to ensure high-quality and consistent input data.A robust data augmentation strategy enhances model generalizability, including rotation, brightness and contrast adjustments, Gaussian noise addition, random cropping, and horizontal flipping.The core innovation of this framework is the optimized VGG19 architecture, which replaces conventional 5 5 convolution operations with two consecutive 3 3 convolutions.This modification maintains the receptive field while significantly reducing the number of parameters, improving computational efficiency, and strengthening hierarchical feature extraction.The model was rigorously evaluated on the UTKFace and Adience datasets, achieving state-of-the-art accuracies of 97.3% and 95.3% for gender classification and 94.7% and 92.7% for age classification, respectively.These results surpass existing models' accuracy, F1 score, and computational efficiency.The findings highlight the robustness of the proposed framework in real-world, uncontrolled environments.Its scalability and superior performance make it a promising solution for demographic classification tasks in various applications.Future research will focus on real-time deployment, validation of additional datasets, and exploring hybrid architectures that integrate convolutional neural networks (CNNs) with Vision Transformers (ViTs) for further performance improvements.