Abstract
The demand for automatic gender and age prediction has surged with the proliferation of social media and online platforms. Despite advancements in related fields like facial recognition, the effectiveness of current technologies in real-world scenarios remains limited. This paper explores the application of deep convolutional neural networks (CNNs) to address this challenge. Our proposed method involves a five-step process: facial recognition, background removal, face alignment, application of multiple CNN models, and a voting mechanism to enhance prediction accuracy. We evaluate our approach using the recent Audience-Face benchmark dataset, focusing on gender detection and age estimation. The implementation is carried out using Python. Keywords: Convolutional neural networks (CNN), Deep learning, Facial recognition, Computer vision, Region of interest (ROI).
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