Gender and Race Recognition System with Age Estimation using Convolutional Neural Network

Gender, race, and age recognition systems employ machine learning and computer vision techniques to analyze images or video data and provide predictions regarding an individual's gender, race, and age based on their visual characteristics. These systems find extensive utility across various applications, particularly within social media platforms. Gender recognition systems are designed to categorize individuals as either male or female, relying on visual cues extracted from images or videos. Notable visual features used for gender prediction encompass facial characteristics, hairstyle, and attire. Meanwhile, race recognition systems endeavor to discern an individual's racial or ethnic background by analyzing visual traits such as facial features, skin tone, and related attributes. Age recognition systems estimate an individual's age, often within a specified age range, leveraging characteristics like facial wrinkles, skin texture, and hair color. In our research, gender, race and age recognition is carried out by the single model training. A standardized UTK facial image dataset is employed to train our model using Convolutional Neural Networks (CNNs), achieving an approximate model accuracy of 93%.