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%.

I. Introduction

The interpretation of human qualities from visual data is at the forefront of innovation and societal effect in today's technologically driven society. The ability to reliably detect gender, ethnicity, and age from facial photographs has become a crucial component of computer vision research, from improving security procedures to altering marketing methods. However, despite its immense potential, this subject faces a variety of problems and exciting prospects that necessitate further study. Through an exhaustive evaluation of existing approaches, this study goes on a critical quest to delve into the delicate domain of age, race, and gender classification. Beyond the obvious applications, there are far-reaching implications for human-computer interaction and the creation of intelligent systems capable of sensing and responding to complex human characteristics. This research aims to not only illuminate the strengths and limitations of current methods, but also chart a course toward pioneering advancements in the field by unraveling the nuances of CNN-based approaches, including network architecture, training methodologies, and dataset augmentation techniques. Through an exhaustive evaluation of existing approaches, this study goes on a critical quest to delve into the delicate domain of age, race, and gender classification. Beyond the obvious applications, there are far-reaching implications for human-computer interaction and the creation of intelligent systems capable of sensing and responding to complex human characteristics. This research aims to not only illuminate the strengths and limitations of current methods, but also chart a course toward pioneering advancements in the field by unraveling the nuances of CNN-based approaches, including network architecture, training methodologies, and dataset augmentation techniques. Furthermore, the value of this investigation goes beyond algorithmic performance. It includes the ethical and social aspects of accurately interpreting human qualities using computational systems. This work aims to offer light on the larger consequences of deploying such technologies in varied societal situations by critically assessing experimental outcomes on benchmark datasets