Abstract:
Interest in automatically classifying people's age and gender from face photographs has grown since the introduction of social media. Thus, the age and gender categorization process is an essential step in many applications, including interest group targeting, aging analysis, face verification, and ad targeting. However, the majority of age and gender classification systems still have some issues when used in practical settings.
This study employs several convolutional neural networks (CNN) for the categorization of age and gender. The suggested approach consists of five stages: multiple CNN, face alignment, background reduction, face identification, and voting systems. In terms of structure and depth, the multiple CNN model has three distinct CNNs; the objective of this variation is to extract distinct characteristics for each. This is accomplished by using machine learning models and algorithms that have been trained on big datasets of photos of individuals of various ages.
Our project, " Gender and Age Detection Through Image Processing Technique," makes use of intelligent technologies to infer an individual's age from their photos. Our goal is to educate computers to identify age-related facial traits so they may be used for demographic analysis, content recommendation, and targeted advertising. Our initiative seeks to be accurate and useful in real-world scenarios by utilizing cutting-edge methodologies. It's similar to teaching computers to make age predictions based only on a person's picture. true-time age prediction is where the true magic occurs. Consider submitting a photo, and our algorithm will provide you with an age estimate right away. In addition to this wow element, there are useful uses
