Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images
Abstract
Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications.
Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results.
Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore.
Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested.
Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.
References
[2] M. Irsan, S. Ramadhan, and S. A. Murad, “Pendeteksian Wajah Menggunakan Algoritma Convolutional Neural Network Dalam Menghitung Jumlah Mahasiswa,” Kumpul. J. Ilmu Komput., vol. 8, no. 3, pp. 279–290, 2021.
[3] H. Sulaiman, Z. Zainuddin, and S. Sahibu, “Sistem Deteksi Wajah Untuk Identifikasi Kehadiran Mahasiswa Dengan Menggunakan Metode Eigenface Pca,” J. Ris. Inform., vol. 1, no. 2, pp. 97–106, 2019, doi: 10.34288/jri.v1i2.36.
[4] P. Choirina and R. A. Asmara, “Deteksi Jenis Kelamin Berdasarkan Citra Wajah Jarak Jauh Dengan Metode Haar Cascade Classifier,” J. Inform. Polinema, vol. 2, no. 4, 2016, doi: 10.33795/jip.v2i4.77.
[5] B. Pribadi and M. Naseer, “Sistem Klasifikasi Jenis Kendaraan Melalui Teknik Olah Citra Digital,” Setrum Sist. Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 3, no. 2, p. 103, 2016, doi: 10.36055/setrum.v3i2.505.
[6] T. Susim and C. Darujati, “Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV,” J. Heal. Sains, vol. 2, no. 3, pp. 534–545, 2021, doi: 10.46799/jsa.v2i3.202.
[7] W. I. Suartika, Y. A. Wijaya, and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, p. 76, 2016, [Online]. Available: http://repository.its.ac.id/48842/
[8] W. Setiawan, “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus,” J. Simantec, vol. 7, no. 2, pp. 50–51, 2020, doi: 10.21107/simantec.v7i2.6551.
[9] N. U. Oktaviana and Y. Azhar, “Garbage Classification Using Ensemble DenseNet169,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1207–1215, 2021, doi: 10.29207/resti.v5i6.3673.
[10] A. Supriyanto, W. A. Kusuma, and H. Rahmawan, “Klasifikasi Kanker Tumor Payudara Menggunakan Arsitektur Inception-V3 Dan Algoritma Machine Learning,” J. Al-AZHAR Indones. SERI SAINS DAN Teknol., vol. 7, no. 3, 2022, doi: 10.36722/sst.v7i3.1284.
[11] L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” arxiv Comput. Vis., vol. 1, no. 2, p. 5, 2017, doi: https://doi.org/10.48550/arXiv.1712.04621.
[12] M. A. Rohman and D. Arifianto, “Penerapan Metode Euclidean Probality dan Confusion Matrix dalam Diagnosa Penyakit Koi,” J. Smart Teknol., vol. 2, no. 2, pp. 122–130, 2021, [Online]. Available: file:///C:/Users/dokta/Downloads/4992-14056-2-PB.pdf
[13] M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/Access.2022.3151048.
[14] L. Hamel, Model Assessment with ROC Curves. 2011. doi: 10.4018/978-1-60566-010-3.
[15] B. P. Pratiwi, A. S. Handayani, and S. Sarjana, “Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi Wsn Menggunakan Confusion Matrix,” J. Inform. Upgris, vol. 6, no. 2, pp. 66–75, 2021, doi: 10.26877/jiu.v6i2.6552.
[16] J. Miao and W. Zhu, “Precision–recall curve (PRC) classification trees,” Evol. Intell., vol. 15, no. 3, pp. 1545–1569, 2022, doi: 10.1007/s12065-021-00565-2.
[17] R. A. Asmara, B. S. Andjani, U. D. Rosiani, and P. Choirina, “Klasifikasi Jenis Kelamin Pada Citra Wajah Menggunakan Metode Naive Bayes,” J. Inform. Polinema, vol. 4, no. 3, p. 212, 2018, doi: 10.33795/jip.v4i3.209.
[18] Y. Harjoseputro, “Convolutional Neural Network (Cnn) Untuk Pengklasifikasian Aksara Jawa,” Buana Inform., vol. 1, no. 1, 2018.
[19] R. Febriawan, “Klasifikasi Gender Pada Citra Wajah Menggunakan Convolutional Neural Network Dan Transfer Learning,” https://repository.uinjkt.ac.id, Jakarta, 2022.
[20] A. Rohim, Y. A. Sari, and Tibyani, “Convolution neural network (cnn) untuk pengklasifikasian citra makanan tradisional,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 7, pp. 7038–7042, 2019, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5851/2789
