Abstract
The article discusses the efficiency of convolutional neural networks in solving the problem of face recognition of tennis players. The characteristics of training and accuracy on a test set for networks of various architectures are compared. Application of weight drop out methods and data augmentation to eliminate the effect of retraining is also considered. Finally, the transfer learning from other known networks is used. It is shown how, for initial data, it is possible to increase recognition accuracy by 25% compared to a typical convolutional neural network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shilpi, S., Prasad, S.V.: Techniques and challenges of face recognition: a critical review. Proc. Comput. Sci. 143, 536–543 (2018)
Zhang, Y., Lv, P., Lu, X.: A deep learning approach for face detection and location on highway. In: IOP Conference Series: Materials Science and Engineering, vol. 435, p. 012004 (2018). https://doi.org/10.1088/1757-899x/435/1/012004
Ye, L., Ying, W., Liu, H., Hao, J.: Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves. Neurocomputing 275, 1295–1307 (2018)
Logan, A.J., Gordon, G.E., Loffler, G.: Contributions of individual face features to face discrimination. Vis. Res. 137, 29–39 (2017)
Guillaume, D., Chao, X., Kishore, S.: Face recognition in mobile phones. Depart. Electr. Eng. Stanford Univ. (2010)
Guillaume, D.: Facial recognition tech secures enterprise access control. Biometric Technol. Today 2017(10), 2–3 (2017). https://doi.org/10.1016/S0969-4765(17)30145-5
Geng, D., Fei, S., Anni, C.: Face recognition using SURF features. Proc. SPIE – Int. Soc. Optic. Eng. 2, 6–12 (2009). https://doi.org/10.1117/12.832636
Chen, Z., Lam, O., Jacobson, A., Milford, M.: Convolutional Neural Network-based Place Recognition. Access mode: https://arxiv.org/ftp/arxiv/papers/1411/1411.1509.pdf
Boubacar, B.T., Kamsu-Foguem, B., Tangara, F.: Deep convolution neural network for image recognition. Ecol. Inform. 48, 257–268 (2018). https://doi.org/10.1016/j.ecoinf.2018.10.002
Coşkun, M., Uçar, A., Yıldırım, O., Demir, Y.: Face recognition based on convolutional neural network. MEES (2017). https://doi.org/10.1109/MEES.2017.8248937
Andriyanov, N.A., Volkov, Al.K., Volkov, An.K., Gladkikh, A.A. Danilov, S.D.: Automatic x-ray image analysis for aviation security within limited computing resources. In: IOP Conference Series: Materials Science and Engineering, vol. 862, p. 052009 (2020). https://doi.org/10.1088/1757-899x/862/5/052009
Vasil’ev, K.K., Dement’ev, V.E., Andriyanov, N.A.: Application of mixed models for solving the problem on restoring and estimating image parameters. Pattern Recogn. Image Anal. 26(1), 240–247 (2016). https://doi.org/10.1134/S1054661816010284
Andriyanov, N.A., Vasiliev, K.K., Dementiev, V.E.: Anomalies detection on spatially inhomogeneous polyzonal images. CEUR Workshop Proc. 1901, 10–15 (2017). https://doi.org/10.18287/1613-0073-2017-1901-10-15
Vasiliev, K.K., Andriyanov, N.A.: Synthesis and analysis of doubly stochastic models of images. CEUR Workshop Proc. 2005, 145–154 (2017)
Andriyanov, N.A., Dementiev, V.E.: Developing and studying the algorithm for segmentation of simple images using detectors based on doubly stochastic random fields. Pattern Recogn. Image Anal. 29(1), 1–9 (2019). https://doi.org/10.1134/S105466181901005X
Tanwir, K.: Computer Vision - Detecting objects using Haar Cascade Classifier. Electronic resource. Access mode: https://towardsdatascience.com/computer-vision-detecting-objects-using-haar-cascade-classifier-4585472829a9 (2019)
Buslaev, A., Parinov, A., Khvedchenya, E., Iglovikov, V., Kalinin, A.: Albumentations: fast and flexible image augmentations. arXiv:1809.06839v1 [cs.CV] (2018)
Andriyanov, N.A., Dement’ev, V.E.: Application of mixed models of random fields for the segmentation of satellite images. CEUR Workshop Proc. 2210, 219–226 (2018)
Andriyanov, N.A.: Software complex for representation and processing of images with complex structure. CEUR Workshop Proc. 2274, 10–22 (2018)
Electronic resource. Access mode: https://www.kaggle.com/c/dogs-vs-cats
Acknowledgement
The study was funded by RFBR, Project № 19-29-09048, RFBR and Ulyanovsk Region, Project № 19-47-730011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Andriyanov, N., Dementev, V., Tashlinskiy, A., Vasiliev, K. (2021). The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-68821-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68820-2
Online ISBN: 978-3-030-68821-9
eBook Packages: Computer ScienceComputer Science (R0)