Deep Learning based age and gender detection using facial images

Recognizing individuals is an innate and vital aspect of human interaction, from identifying loved ones to acquaintances in professional settings. Age and gender serve as fundamental attributes in this process. As artificial intelligence (AI) continues to integrate into various aspects of our lives, the field of computer science has witnessed a surge in demand for automated demographic analysis based on facial recognition. This paper aims to fulfill this need by analyzing the demographics of populations through facial images, predicting both age and gender. The study focuses on age classification, gender classification, and age estimation from static facial images. Two distinct methodologies are explored: one employing deep Convolutional Neural Networks (CNNs) and the other utilizing transfer learning. The latter approach entails an exploration of various backbone models, including VGG16, ResNet50V2, ResNet152V2, Xception, InceptionV3, MobileNetV3Small, and MobileNetV3Large, to determine the most suitable architecture for robust age and gender classification models. Through thorough investigation and analysis, this research contributes to the advancement of facial recognition technology, offering insights for practical applications in diverse domains such as forensics, missing person identification, personalized marketing, and security surveillance.