Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning
Abstract
Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face.
Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively.
Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach.
Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery.
Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.
References
[2] Muthmainnah Muthmainnah and Rahayu Rahayu, “Pengenalan Bentuk Wajah Manusia Pada Citra Menggunakan Metode Fisherface,” TECHSI - Jurnal Teknik Informatika, vol. 8, no. 1, pp. 215–225, Mar. 2019, doi: https://doi.org/10.29103/techsi.v8i1.128.
[3] Wisuwat Sunhem and Kitsuchart Pasupa, “An approach to face shape classification for hairstyle recommendation,” Feb. 2016, doi: https://doi.org/10.1109/icaci.2016.7449857.
[4] S. Khan, E. Ahmed, M. H. Javed, S. A. A Shah, and S. U. Ali, “Transfer Learning of a Neural Network Using Deep Learning to Perform Face Recognition,” IEEE Xplore, Jul. 01, 2019. https://ieeexplore.ieee.org/document/8940754
[5] A. F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 257–267, Mar. 2001, doi: https://doi.org/10.1109/34.910878.
[6] F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” Journal of Informatics and Computer Science (JINACS), vol. 1, no. 02, pp. 104–108, Jan. 2020, doi: https://doi.org/10.26740/jinacs.v1n02.p104-108.
[7] Fathan Arsyadani and Aji Purwinarko, “Implementation of Synthetic Minority Oversampling Technique and Two-phase Mutation Grey Wolf Optimization on Early Diagnosis of Diabetes using K-Nearest Neighbors,” vol. 1, no. 1, pp. 9–17, Mar. 2023, doi: https://doi.org/10.15294/rji.v1i1.64406.
[8] Yahya Nur Ifriza, Trisni Wulandari Veronika, Trisni Suryarini, and Antonius Supriyadi, “The Modeling Of Laboratory Information Systems In Higher Education Based On Enterprise Architecture Planning (EAP) For Optimizing Monitoring And Equipment Maintenance,” Matrix, vol. 13, no. 1, pp. 1–11, Mar. 2023, doi: https://doi.org/10.31940/matrix.v13i1.1-11.
[9] A. B. Susilo and E. Sugiharti, “Accuracy Enhancement in Early Detection of Breast Cancer on Mammogram Images with Convolutional Neural Network (CNN) Methods using Data Augmentation and Transfer Learning,” Journal of Advances in Information Systems and Technology, vol. 3, no. 1, pp. 9–16, Apr. 2021, doi: https://doi.org/10.15294/jaist.v3i1.49012.
[10] A. Adimas and S. Y. Irianto, “Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval,” Scientific Journal of Informatics, vol. 8, no. 2, pp. 176–182, Nov. 2021, doi: https://doi.org/10.15294/sji.v8i2.27865.
[11] Pulung Nurtantio Andono and Christy Atika Sari, “Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform,” Scientific Journal of Informatics, vol. 9, no. 2, pp. 179–188, Nov. 2022, doi: https://doi.org/10.15294/sji.v9i2.34173.
[12] S. Karimpouli and P. Tahmasebi, “Image-based velocity estimation of rock using Convolutional Neural Networks,” Neural Networks, vol. 111, pp. 89–97, Mar. 2019, doi: https://doi.org/10.1016/j.neunet.2018.12.006.
[13] H. Stone, “Calibrating Rough Volatility Models: A Convolutional Neural Network Approach,” SSRN Electronic Journal, 2019, doi: https://doi.org/10.2139/ssrn.3327135.
[14] A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images,” Computer Methods and Programs in Biomedicine, vol. 196, p. 105581, Nov. 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105581.
[15] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, Jun. 2014, doi: https://doi.org/10.1109/cvpr.2014.81.
[16] M. Jogin, Mohana, M. S. Madhulika, G. D. Divya, R. K. Meghana, and S. Apoorva, “Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning,” IEEE Xplore, 2018. https://ieeexplore.ieee.org/document/9012507.
[17] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature Pyramid Networks for Object Detection,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, doi: https://doi.org/10.1109/cvpr.2017.106.
[18] A. Wang, W. Wang, J. Liu, and N. Gu, “AIPNet: Image-to-Image Single Image Dehazing With Atmospheric Illumination Prior,” IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 381–393, Jan. 2019, doi: https://doi.org/10.1109/tip.2018.2868567.
