REAL TIME AGE AND GENDER CLASSIFIER

Abstract

Gender is still a prominent element of our personalities. It also plays an important role in our social lives. Artificial intelligence age forecasts have applications in a variety of disciplines, including smart human-machine interface development, health, cosmetics, and electronic commerce, among others. The ability to estimate people's sex and age from their face photographs is a current and active research topic. The researchers proposed a number of solutions to the problem, but the criteria and actual results are still insufficient. This paper proposes a statistical pattern recognition strategy to solve this challenge. In the proposed method, a Deep Learning technique called Convolutional Neural Network (ConvNet / CNN) is employed to extract features. CNN takes input images and assigns a value to distinct characteristics / elements of the image (learnable weights and biases) and can distinguish between them. Other classification techniques require far more pre-processing than ConvNet. While the filters are created by hand using simple methods, ConvNets can be trained to learn these filters and features. Face photos of individuals were trained with convolutional neural networks in this study, and age and sex were accurately predicted with a high rate of success. There are around 20,000 photos with age and gender. Poses, facial expressions, lighting, occlusion, and resolution are all represented in the photos.