Application of MobileNetV1 and DenseNet-121 CNN Architectures for Eye-Based Gender Classification
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Abstract
Gender classification is an important field in biometric identification systems that plays a vital role in security, forensics, and human–computer interaction. Human eye images are a promising visual object for gender classification because they contain distinct anatomical features that differ between males and females. This study aims to implement and evaluate two Convolutional Neural Network (CNN) architectures, namely MobileNetV1 and DenseNet-121, for gender classification based on human eye images. The dataset used was obtained from the Kaggle platform, consisting of 11,525 eye images, with 6,323 male and 5,202 female samples. The research process involved several stages, including pre-processing, data splitting, augmentation using Affine transformations (rotation and translation), as well as model training and evaluation. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that both architectures were capable of performing gender classification effectively, although differences in performance were observed. The best accuracy was achieved by MobileNetV1 with a rotation scenario of 92.49%, while DenseNet-121 obtained 86.84% with a combined rotation and translation scenario. This research is expected to contribute to the development of efficient and accurate eye image–based gender classification systems using deep learning approaches.
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References
I. P. E. Sutariawan, G. R. Dantes, and K. Y. E. Aryanto, “Segmentasi mata katarak pada citra medis menggunakan metode operasi morfologi,” Jurnal Ilmu Komputer Indonesia, vol. 3, no. 1, pp. 23–31, 2018.
A. Triwijoyo, “Pemrosesan citra untuk pengenalan pola biometrik,” Jurnal Ilmiah Informatika, vol. 4, no. 2, pp. 77–85, 2019.
S. F. Handono, F. T. Anggraeny, and B. Rahmat, “Implementasi convolutional neural networks (CNN) untuk deteksi retinopati diabetik,” Jurnal Informatika dan Sistem Informasi (JIFoSI), vol. 1, no. 1, pp. 669–678, 2020.
M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi metode convolutional neural network menggunakan arsitektur LeNet-5 untuk pengenalan doodle,” Jurnal Algoritme, vol. 1, no. 1, pp. 45–56, 2020.
A. Kuehlkamp and K. W. Bowyer, “Predicting gender from iris texture may be harder than it seems,” in Proc. IEEE Winter Conf. Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, pp. 904–912, Jan. 2019.
D. Bobeldyk and A. Ross, “Analyzing covariate influence on gender and race prediction from near-infrared ocular images,” IEEE Access, vol. 7, no. 1, pp. 7905–7919, 2018.
A. Rehman, M. N. Sajid, M. R. U. R. Talib, M. S. A. Malik, and A. Ullah, “Authentication through gender classification from iris images using support vector machine,” Expert Systems, vol. 38, no. 5, pp. e12710, 2021.
B. M. S. Hasan and R. J. Mstafa, “Exploring the feasibility of deep learning techniques for accurate gender classification from eye images,” Science Journal of University of Zakho, vol. 10, no. 4, pp. 222–234, 2022.
R. Pradana and D. Wijiyanto, “Identifikasi jenis kelamin otomatis berdasarkan mata menggunakan CNN dan Haar cascade classifier,” Jurnal Informatika dan Sistem Cerdas, vol. 7, no. 1, pp. 44–51, 2024.
R. D. Wicaksono and G. F. Shidiq, “Comparison of KNN and CNN algorithms for gender classification based on eye images,” Scientific Journal of Informatics, vol. 11, no. 4, pp. 915–924, 2024.
B. Patil and M. Hangarge, “Pre-trained convolutional neural networks for gender classification,” International Journal of Computer Vision and Signal Processing, vol. 13, no. 1, pp. 33–40, 2023.
D. Kurniawan and A. Irsyad, “Perbandingan metode K-nearest neighbor dan naive Bayes untuk klasifikasi gender berdasarkan mata,” Jurnal Sains Komputer dan Informatika, vol. 6, no. 2, pp. 155–162, 2022.
J. Tapia, C. Aravena, and C. Busch, “Deep iris: Deep learning for gender classification through iris patterns,” Sensors, vol. 19, no. 19, pp. 4061, Sep. 2019.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient convolutional neural networks for mobile vision applications,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA, pp. 684–692, 2017.
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 4700–4708, 2017.
A. Gani, “Transformasi affine pada bidang,” Jurnal Matematika Unisba, vol. 1, no. 1, pp. 1–8, 2018.
B. Purnomo, R. Santosa, and D. Widodo, “Augmentasi citra dengan transformasi affine untuk peningkatan robustness CNN,” Jurnal Teknologi dan Sistem Informasi, vol. 10, no. 2, pp. 101–109, 2022.
Pavelbiz, “Eyes-RTTE: Female and male eyes dataset,” Kaggle, Dataset, 2021. [Online]. Available: https://www.kaggle.com. [Accessed: Dec. 2024].
I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi citra digital bumbu dan rempah dengan algoritma convolutional neural network (CNN),” Jurnal Gaussian, vol. 9, no. 3, pp. 273–282, 2020.
A. Rahman, A. P. Windarto, and A. P. U. Siahaan, “Evaluasi metode klasifikasi menggunakan confusion matrix,” Jurnal Sistem Informasi, vol. 13, no. 2, pp. 45–52, 2017.
M. Resa, A. Yudianto, and H. Al Fatta, “Analisis pengaruh tingkat akurasi klasifikasi citra wayang dengan algoritma convolutional neural network,” Jurnal Teknologi Informasi, vol. 4, no. 2, pp. 182–190, 2020.
T. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 60, pp. 1–48, 2019.
S. Lakra, P. Singh, and A. Kumar, “Gender classification using convolutional neural networks for biometric applications,” Procedia Computer Science, vol. 167, pp. 2530–2537, 2020.
D. Darmatasia and A. M. Syafar, “Implementasi convolutional neural network untuk klasifikasi tanaman rimpang secara virtual,” Jurnal INSTEK, vol. 8, no. 1, pp. 122–131, 2023.
P. Kumari and K. R. Seeja, “A novel periocular biometrics solution for authentication during COVID-19 pandemic situation,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 11, pp. 8427–8440, 2021.