Application of Convolutional Neural Networks and Vision Transformer Models for Age and Gender Detection

This research explore the integration and application of advanced deep learning models, specifically Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) models, in the field of age and gender detection. The study begins by outlining the significance and challenges of accurate age and gender detection in various domains such as targeted advertising, security, and humancomputer interaction. It then delves into technical aspects of CNNs and ViTs, elucidating their architecture, working principles, and suitability for image-based task. The proposed techniques used in this study was able to differentiate between the following age groups: 0–15, 15–20, 20–25, 25–30, 30–35, and 40. The purpose is to offer a technique for creating and implementing accurate categorization and age estimation systems capable of operating and achieving high accuracy by integrating and applying a variety of feature extractors and algorithms. Pre-processing is evaluating preliminary data, configuring it, and transforming it to a standard format. The feature extraction component of the age and gender prediction technique is crucial. Three different extraction methods (ResNet 50, ViT Small, and ViT Base) are used in this section. Convolutional Neural Network (CNN) and Vision Transformer (ViT) classifiers were used. This optional component of pattern recognition system design focuses on system accuracy. To assess pattern recognition system performance, several approaches are employed, including Mean Absolute Error (MAE), Cumulative Score (CS), Leave-One-Out Cross-validation, and Confusion Matrix. In this study, however, gender and age prediction were tested using the Confusion Matrix and Mean Absolute Error (MAE). Python was the programming language utilized in this study. Python is a high-level, general-purpose programming language that is interpreted. Precision, recall, f1score and accuracy were the performance matrices used. A precision of 99% was achieved for male classification while 98% precision was achieved for female classification. The result obtained an accuracy of 98% for the model used.