Abstract: Gender classification from facial photos is difficult due to the presence of a complex background, object occlusion, and varying lighting conditions. Face photos can be used for a variety of applications, including expression analysis, recognition, and tracking. This research investigates two deep learning-based approaches for gender classification using face photos. These approaches include CNN and Alex Net. Experiments were conducted to assess the effectiveness of both models in identifying male and female classes from facial photographs. The results indicate that both techniques were effective for gender classification. Additionally, a comparison study was carried out between these two models and a few well-known techniques for classifying gender.
Keywords: gender classification, gender recognition, CNN, Alex Net, Deep learning
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DOI:
10.17148/IJARCCE.2024.13468
