Optimizing Gender Classification Accuracy in Facial Images Using Data Augmentation and Inception V-3


  • Juliansyah Putra Tanjung Universitas Prima Indonesia
  • Mhd. Rio Faldi Universitas Prima Indonesia
  • Haggai Sitompul Universitas Prima Indonesia
  • Muhammad Ridho Universitas Prima Indonesia
  • Jojor Putri Ambarita Universitas Prima Indonesia




Gender Classification; Facial Recognition Technology; Data Augmentation; Inception V-3; CNN.


In the digital era, facial recognition technology plays a crucial role in various applications, including gender classification. However, challenges such as variations in expressions and face positions, as well as differences in features between men and women, make this task formidable. This study aims to enhance the accuracy of gender classification using the Inception V-3 method and the Convolutional Neural Network (CNN), along with data augmentation techniques. The Inception V-3 method was chosen for its superiority in accuracy and speed. In contrast, the CNN model was selected in this study as a comparison and due to its algorithmic advantages in learning and extracting high-level features from images, including facial images, which are crucial for tasks such as gender classification. The data augmentation techniques in this study include rescaling, rotation, width and height shifts, shear range, zoom, horizontal flip, and fill method for model accuracy in gender classification with a small dataset. The study results indicate that the Inception V-3 model provides better accuracy (99.31%) in gender classification compared to the CNN model (81.31%). This conclusion underscores that the use of the Inception V-3 method with data augmentation techniques can improve the accuracy of gender classification in facial images.

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Ahmed, T. U., Hossain, S., Hossain, M. S., ul Islam, R., & Andersson, K. (2019). Facial expression recognition using convolutional neural network with data augmentation. 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (IcIVPR), 336–341.

Ajit, A., Acharya, K., & Samanta, A. (2020). A Review of Convolutional Neural Networks. International Conference on Emerging Trends in Information Technology and Engineering, Ic-ETITE 2020. https://doi.org/10.1109/ic-ETITE47903.2020.049


Arifandi, A. (2022). Identifikasi dan Prediksi Umur serta Jenis Kelamin Berdasarkan Citra Wajah Menggunakan Algoritma Convolutional Neural Network (CNN). RAINSTEK: Jurnal Terapan Sains & Teknologi, 4(2), 89–96.

Asmara, R. A., Andjani, B. S., Rosiani, U. D., & Choirina, P. (2018). Klasifikasi Jenis Kelamin Pada Citra Wajah Menggunakan Metode Naive Bayes. Jurnal Informatika Polinema, 4(3), 212.

Bharadwaj, Y. S. S. (2021). Advanced Deep Learning Techniques. Advanced Deep Learning for Engineers and Scientists: A Practical Approach, 145–181.

Budiman, M. A. (2021). The effect of audit opinions, implementation of audit recommendations, and findings of state losses on corruption levels within ministries and institutions in the Republic of Indonesia. Jurnal Tata Kelola Dan Akuntabilitas Keuangan Negara, 7(1), 113–129.

Jiang, Z. (2020). Face gender classification based on convolutional neural networks. 2020 International Conference on Computer Information and Big Data Applications (CIBDA), 120–123.

Muhathir, Dwi Ryandra, M. F., Syah, R. B. Y., Khairina, N., & Muliono, R. (2023). Convolutional Neural Network (CNN) of Resnet-50 with Inceptionv3 Architecture in Classification on X-Ray Image. Computer Science On-Line Conference, 208–221.

Punia, S. K., Kumar, M., Stephan, T., Deverajan, G. G., & Patan, R. (2021). Performance analysis of machine learning algorithms for big data classification: Ml and ai-based algorithms for big data analysis. International Journal of E-Health and Medical Communications (IJEHMC), 12(4), 60–75.

Rosiani, U. D., Asmara, R. A., & Laeily, N. (2019). Penerapan Facial Landmark Point Untuk Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah. Jurnal Informatika Polinema, 6(1), 55–60.

Sabili, S., Rachmadi, R. F., & Yuniarno, E. M. (2021). Verifikasi Wajah Menggunakan Deep Metric Learning pada Data Wajah dengan Disparitas Umur yang Besar. Jurnal Teknik ITS, 10(2), A432–A437.

Selitskiy, S., Christou, N., & Selitskaya, N. (n.d.). Learning Incorrect Verdict Patterns with the Meta-learning Supervisor ANN on the Established Face Recognising CNN Models.

Setiawan, W. (2020). Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus. Jurnal Simantec, 7(2), 48–53. https://doi.org/10.21107/simantec.v7i2.6551

Wahyu, M., Santoso, B., Wihandika, R. C., & Rahman, M. A. (2019). Ekstraksi Ciri untuk Klasifikasi Jenis Kelamin berbasis Citra Wajah menggunakan Metode Compass Local Binary Patterns. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(11).

Zhou, Y., Chang, H., Lu, Y., Lu, X., & Zhou, R. (2021). Improving the Performance of VGG through Different Granularity Feature Combinations. IEEE Access, 9, 26208–26220. https://doi.org/10.1109/ACCESS.2020.3031908


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How to Cite

Tanjung, J. P., Faldi, M. R., Sitompul, H., Ridho, M., & Ambarita, J. P. . (2023). Optimizing Gender Classification Accuracy in Facial Images Using Data Augmentation and Inception V-3. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2627-2634. https://doi.org/10.33395/sinkron.v8i4.12785