Framework for Gender Detection Using Facial Countenances
Shyla Shyla, Shalu Shalu, Mohit Dayal · 2024
This research paper delves into the domain of gender detection from facial images, presenting an innovative approach leveraging state-of-the-art machine learning techniques. With the aim of achieving heightened accuracy and robustness, the study builds upon convolutional neural networks (CNNs), a potent tool in computer vision. The investigation critically examines the limitations of existing methodologies, particularly those reliant on support vector machines (SVM) and datasets like FERET. Notably, these approaches are constrained by controlled environments, emphasizing the need for advancements that transcend such limitations. The research unfolds with a threefold objective: to scrutinize technological advancements in artificial intelligence, particularly within computer vision, for precise gender prediction; to comprehend the intricate psychological and sociological factors influencing gender perception; and to scrutinize the ethical implications accompanying the deployment of gender recognition technology. The code implementation showcases a meticulous training process involving data preparation, image augmentation, and the formulation of a CNN model. The evaluation phase encompasses traditional metrics such as accuracy and loss, complemented by real-time gender detection using a webcam. As the technological landscape evolves, this research underscores the significance of responsible development and deployment, addressing ethical considerations and ensuring inclusivity in gender recognition technologies. The findings contribute to the ongoing discourse on gender detection, providing insights into cutting-edge methodologies and ethical dimensions that shape the future of facial image analysis.