The Effects of Augmented Training Dataset on Performance of Convolutional Neural Networks in Face Recognition System
Mehmet Ali Kutlugün, Yahya Şirin, Mehmet Ali Karakaya
Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 929–932 (2019)
Abstract. Nowadays, deep learning methods have been used in many areas such as big data analysis, speech and image processing with the increasing processing power and the development of graphics processors. In particular, face recognition systems have become one of the most important research topics in biometry. Light direction, reflection, emotional and physical changes in facial expression are the main factors in face recognition systems that make recognition difficult. Training of the system with the available data in small data sets is an important factor that negatively affects the performance. The Convolutional Neural Network (CNN) model is a deep learning architecture used for large amounts of training data. In this study, a small number of employee images set of a small-scale company has been increased by applying different filters. In addition, it has been tried to determine which data augmentation options have more effect on face recognition. Thus, non-real-time face recognition has been performed by training with new augmented dataset of each picture with many features.
- Bilgiç, A. et al., “Face recognition classifier based on dimension reduction in deep learning properties.“ Signal Processing and Communications Applications Conference (SIU), 2017 25th. IEEE, 2017.
- L. Deng and D. Yu, “Deep Learning: Methods and Applications” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.
- H. A. Song and S.-Y. Lee, “Hierarchical Representation Using NMF” in International Conference on Neural Information Processing, pp. 466–473, 2013.
- Şeker, A. et al., “A Study on Deep Learning Methods and Applications“, Gazi Journal of Engineering Sciences, 3.3: 47-64,2017.
- Ian Goodfellow et al. “Deep Learning”, MIT Press, 2016.
- Çalık, N. et al., “Signature recognition application based on deep learning” Signal Processing and Communications Applications Conference (SIU), 2017 25th. IEEE, 2017.
- Ranzato, Y. M. et al., “Sparse Feature Learning for Deep Belief Networks”, Proc. Adv. Neural Inf. Process. Syst., vol. 20, pp. 1185-1192, 2007.
- Scherer, D. et al., “Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition”, International Conference on Artificial Neural Network, pp. 92-101, 2010.
- Yang, Hu et al., “When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks For Face Recognition”, In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp142-150, 2015.
- Chen, X. W. and Lin X., “Big Data Deep Learning: Challenges and Perspectives”, IEEE, vol. 2, pp. 514-525, 2014.
- Krizhevsky, A. et al., “Imagenet classification with deep convolutional neural networks” in Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc.,2012.
- Liu, Si et al., “Matching-cnn meets knn: Quasi-parametric human parsing” Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.
- Vittorio, C. et al., “Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features”, Sensors 19.1:146, 2019.
- Buslaev, A. et al., “Albumentations: fast and flexible image augmentations” arXiv preprint https://arxiv.org/abs/1809.06839, 2018.
- Galdran, A. et al., “Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis”, arXiv preprint https://arxiv.org/abs/1703.03702, 2017.
- Cengil, E. and Çınar, A., “A New Approach for Image Classification: Convolutional Neural Network” European Journal of Technic 6.2,2016.
- Doğan, F. and Türkoğlu, İ., “Comparison of Leaf Classification Performance of Deep Learning Algorithms”, Sakarya University Journal of Computer and Information Sciences 1.1: 10-21, 2018.
- Salman, M., “Integration of Hyperspectral and Lidar Data in Attribute and Decision Levels and Classification with Deep-Curvilinear Neural Networks” Master Thesis, Hacettepe University Institute of Science and Technology, 2018.
- Perlin, H. A. and Lopes, H. S., “Extracting Human Attributes Using A Convolutional Neural Network Approach”, Pattern Recognition Letters, Vol. 68, pp. 250-259, 2015.
- Huang, G.B.; Ramesh, M.; Berg, T.; Learned-Miller, E. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments; Technical Report 07-49; University of Massachusetts: Amherst, MA, USA, 2007.
- Şahin, Ö., “TL recognition for visually impaired people on iOs platform”, Master Thesis, Selçuk University Institute of Science and Technology, 2017.