A Survey of Age and Gender Detector for Retail Analysis

The development of age and gender detectors using Convolutional Neural Networks (CNNs) stands at the forefront of computer vision research, holding significant promise for diverse applications ranging from security to marketing. This survey paper constitutes a comprehensive investigation into the state-of-the-art CNN-based age and gender detection systems. We begin by elucidating the theoretical underpinnings of CNNs, explaining their relevance to this domain, and shedding light on the architecture choices that have proven effective in this context. Subsequently, we delve into dataset considerations, exploring the significance of dataset size, diversity, and quality in training robust models. Through a meticulous analysis of various methodologies employed in model training, testing, and evaluation, this survey outlines best practices and identifies key challenges. Performance metrics, including accuracy, precision, and recall, are scrutinized to provide a holistic perspective on the strengths and limitations of CNN-based detectors. Through a meticulous analysis of various methodologies employed in model training, testing, and evaluation, this survey outlines best practices and identifies key challenges. Performance metrics, including accuracy, precision, and recall, are scrutinized to provide a holistic perspective on the strengths and limitations of CNN-based detectors. In sum, this survey paper serves as a valuable resource for researchers, engineers, and practitioners involved in age and gender detection using CNNs, facilitating a deeper understanding of the technology, its challenges, and its potential applications in a rapidly evolving digital landscape.