EVALUATION OF VISUAL GEOMETRY GROUP16 (VGG16) AND VISUAL GEOMETRY GROUP19 (VGG19) FOR GENDER CLASSIFICATION USING PALM IMAGES
DOI:
https://doi.org/10.33003/fjs-2025-0912-3905Keywords:
Gender classification, palm images, deep learning, convolutional neural networks, Nigerian PalmAbstract
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.
References
Abdul-Al, M., Kyeremeh, G. K., Qahwaji, R., Ali, N. T., & Abd-Alhameed, R. A. (2024). The evolution of biometric authentication: A deep dive into multi-modal facial recognition: A review case study. IEEE Access.
Alausa, D. W., Adetiba, E., Badejo, J. A., Davidson, I. E., Obiyemi, O., Buraimoh, E., ... & Oshin, O. (2022). Contactless palmprint recognition system: a survey. IEEE Access, 10, 132483-132505.
Alshammari, R. F. N., Arshad, H., Abd Rahman, A. H., & Albahri, O. S. (2022). Robotics utilization in automatic vision-based assessment systems from artificial intelligence perspective: A systematic review. IEEE Access, 10, 77537-77570.
Ameen, F. A., & AlShemmary, E. N. (2022). Palmprint Recognition Using VGG16. International Journal on Technical and Physical Problems of Engineering, IJTPEJournal, (53), 65-74
Amrouni, N., Benzaoui, A., & Zeroual, A. (2023). Palmprint Recognition: extensive exploration of databases, methodologies, comparative assessment, and future directions. Applied Sciences, 14(1), 153.
Atoyebi, J. O., Okomba, N. S., Adeyanju, I. A., Akinrotimi, A. O., Ajayi, O. O., & Owolabi, O. O. (2025). Multi-Biometric Recognition Systems: A Comprehensive Review. Adeleke University Journal of Engineering and Technology, 8(1), 010-038.
Bejani, M. M., & Ghatee, M. (2021). A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review, 54(8), 6391-6438.
Bhaidasna, Z. C., Swaminarayan, P. R., & Bhaidasna, H. Z. (2023). Enhancing face recognition with deep learning architectures: a comprehensive review. International journal on recent and innovation trends in computing and communication, 11(9), 164-180.
Das, T., Harshey, A., Mishra, V., & Srivastava, A. (2023). An introduction to biometric authentication systems. In Textbook of forensic science (pp. 805-839). Singapore: Springer Nature Singapore.
Dayarathne, S., Nawarathna, L. S., & Nanayakkara, D. (2021). Determination gender using foot, footprint, hand and hand print measurements in a Sinhalese population in Sri Lanka using supervised learning techniques. Computer Methods and Programs in Biomedicine Update, 1, 100017.
El_Rahman, S., & Alluhaidan, A. S. (2024). Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments. Plos one, 19(2), e0291084.
Elian, H. M., Dousoky, G. M., & Hafez, A. (2025). Developing Deep Learning Based Facial Recognition Technique. Journal of Advanced Engineering Trends, 44(1), 214-220.
Fan, D., Liang, X., Jia, W., & Zhang, D. (2024). Toward large-scale palmprint image analysis by a rich orientation code. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(7), 4113-4125.
Gao, C., Yang, Z., Jia, W., Leng, L., Zhang, B., & Teoh, A. B. J. (2025). Deep Learning in Palmprint Recognition-A Comprehensive Survey. arXiv preprint arXiv:2501.01166.
Habeeb, M. A., Khaleel, Y. L., Ismail, R. D., Al-Qaysi, Z. T., & Ameen, F. N. (2024). Deep Learning Approaches for Gender Classification from Facial Images. Mesopotamian Journal of Big Data, 2024, 185-198.
Ibragimov, A., Segundo, M. P., Sarkar, S., & Bowyer, K. (2024, May). Unveiling Gender Effects in Gait Recognition Using Conditional-Matched Bootstrap Analysis. In 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG) (pp. 1-10). IEEE.
Islam, M. T., Rahman, M. A., Mazumder, M. T. R., & Shourov, S. H. (2024). Comparative Analysis of Neural Network Architectures for Medical Image Classification: Evaluating Performance Across Diverse Models. American Journal of Advanced Technology and Engineering Solutions, 4(01), 01-42.
Jia, W., Ren, Q., Zhao, Y., Li, S., Min, H., & Chen, Y. (2022). EEPNet: An efficient and effective convolutional neural network for palmprint recognition. Pattern Recognition Letters, 159, 140-149.
Katsarou, E., Wild, F., Sougari, A. M., & Chatzipanagiotou, P. (2023). A systematic review of voice-based intelligent virtual agents in EFL education. International Journal of Emerging Technologies in Learning (iJET), 18(10), 65-85.
Khayami, M. (2020). Palmprint Gender Classification Using Deep Learning Methods. West Virginia University.
Mascarenhas, S., & Agarwal, M. (2021, November). A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification. In 2021 International conference on disruptive technologies for multi-disciplinary research and applications (CENTCON) (Vol. 1, pp. 96-99). IEEE.
Miloğlu Ö, Kumbasar N, Turanli Tosun Z, Güller MT, Özbek İY. Gender Classification With Hand-Wrist Radiographs Using the Deep Learning Method. Curr Res Dent Sci. January 2025;35(1):2-7. doi: https://doi.org/10.17567/currresdentsci.1618860
Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M., & Zhang, D. (2023). Biometrics recognition using deep learning: A survey. Artificial Intelligence Review, 56(8), 8647-8695.
Nguyen, K., Proença, H., & Alonso-Fernandez, F. (2024). Deep learning for iris recognition: A survey. ACM Computing Surveys, 56(9), 1-35.
Oulad-Kaddour, M., Haddadou, H., Vilda, C. C., Palacios-Alonso, D., Benatchba, K., & Cabello, E. (2023). Deep learning-based gender classification by training with fake data. IEEE Access, 11, 120766-120779.
Rabea, M., Ahmed, H., Mahmoud, S., & Sayed, N. (2024). IdentiFace: A VGG based multimodal facial biometric system. arXiv preprint arXiv:2401.01227.
Shaheen, R. (2024). Gender and Surveillance: The Implications of Biometric Technologies for Privacy and Equity. Journal of Gender, Power, and Social Transformation, 1(3), 29-35.
Shen, C., Yu, S., Wang, J., Huang, G. Q., & Wang, L. (2024). A comprehensive survey on deep gait recognition: Algorithms, datasets, and challenges. IEEE Transactions on Biometrics, Behavior, and Identity Science.
Song, J., He, M., Zheng, X., Zhang, Y., Bi, C., Feng, J., ... & Shen, B. (2025). Face-based machine learning diagnostics: applications, challenges and opportunities. Artificial Intelligence Review, 58(8), 243.
Sun, X., Zhu, Q., & Qin, Q. (2021). A multi-level convolution pyramid semantic fusion framework for high-resolution remote sensing image scene classification and annotation. IEEE Access, 9, 18195-18208.
Than, P. M., & Nguyen, H. (2025). Efficient Multimodal Biometric Identification via Gabor-Enhanced Attention Networks. Journal of Robotics and Control (JRC), 6(3), 1175-1186.
Uwaechia, A. N., & Ramli, D. A. (2021). A comprehensive survey on ECG signals as new biometric modality for human authentication: Recent advances and future challenges. IEEE Access, 9, 97760-97802.
Yang, W., Wang, S., Hu, J., Tao, X., & Li, Y. (2024). Feature extraction and learning approaches for cancellable biometrics: A survey. CAAI Transactions on Intelligence Technology, 9(1), 4-25.
Yildirim, E. (2024). ResNet-based Gender Recognition on Hand Images. Engineering, Technology & Applied Science Research, 14(6), 17969–17972. https://doi.org/10.48084/etasr.8922
Zakaria, N., & Hassim, Y. M. M. (2024). A review study of the visual geometry group approaches for image classification. Journal of Applied Science, Technology and Computing, 1(1), 14-28.
Zhang, David & Fan, Dandan & Liang, Xu & Zhang, Bob. (2025). Advanced Palmprint Authentication. 10.1007/978-981-96-7101-4.
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Copyright (c) 2025 Muhammad Jumare Haruna, Bello Ibrahim Idris, Darius Tienhua Chinyio, Salisu Musa
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