EVALUATION OF VISUAL GEOMETRY GROUP16 (VGG16) AND VISUAL GEOMETRY GROUP19 (VGG19) FOR GENDER CLASSIFICATION USING PALM IMAGES - Haruna, Muhammad Jumare; Idris, Bello Ibrahim; Chinyio, Darius Tienhua; Musa, Salisu (FUDMA Journal of Sciences, 2025)
The paper focuses on gender classification using biometric features, focusing on palm-based approaches as an alternative to facial-based methods due to advantages like privacy preservation and reduced susceptibility to environmental variations. The study evaluates the performance of VGG16 and VGG19 convolutional neural network architectures for gender classification using a custom Nigerian Palm Gender Classification Dataset, which includes 3,500 high-quality palm images from 1,491 participants across various demographics. Both models were implemented using transfer learning and fine-tuning on the dataset, with a standardized preprocessing pipeline and 5-fold cross-validation for evaluation. VGG19 outperformed VGG16, achieving an overall accuracy of 94.0% compared to 92.0%, with superior precision, recall, and F1-score for both male and female classification. The study confirmed the robustness of the findings through cross-validation and statistical analysis, highlighting VGG19 as the superior architecture for palm-based gender classification, despite increased computational requirements. The research contributes a novel dataset to the biometric community, showcasing the potential for culturally adaptive biometric systems. The implications of these findings are significant for contactless biometric applications in security, access control, and demographic analysis, particularly in diverse cultural contexts. This study provides empirical evidence for optimal architecture selection in palm-based gender classification and emphasizes the importance of considering diverse demographic populations in biometric research.