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Investigation of Methods for Increasing the Efficiency of Convolutional Neural Networks in Identifying Tennis Players


The article is devoted to the study of the effectiveness of the convolutional neural networks (CNNs) application for solving the problem of tennis players face recognition. For ease of analysis, two players were selected: Roger Federer (Switzerland) and Rafael Nadal (Spain). To isolate faces from the publicly available images of the players, it is proposed to use the Haar cascades and the Viola–Jones method. These images are used to train and test convolutional networks with various parameters: architecture, including the number of layers; epochs of learning; optimization methods; and also when applying various regularization methods, including drop out and data augmentation. The use of regularization made it possible to reduce the effect of overfitting. In addition, the efficiency of networks with pretrained layers based on transfer learning methods is investigated. The VGG-16 convolutional network is chosen for the transfer learning. For a large number of different combinations of convolutional networks, metrics are calculated for precision, recall, and accuracy. The average gain for these parameters is 25% with the best set of characteristics for convolutional networks and training. It is also shown in the study that the patterns of applying certain modifications are universal for optical images. In particular, similar architectures and training approaches are also tested for the problem of recognizing cats and dogs on a much larger dataset. The study confirms the average increase in recognition metrics of 26%.

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This study was supported by the the Russian Foundation for Basic Research (project no. 19-29-09048) and by the Russian Foundation for Basic Research and the Government of Ulyanovsk oblast (project no. 19-47-730011).

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Correspondence to N. A. Andriyanov, V. E. Dementev, K. K. Vasiliev or A. G. Tashlinskii.

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The authors declare that they have no conflict of interest.

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Nikita Andreevich Andriyanov, Candidate of Technical Sciences. Born 1990. Graduated from the Radio Engineering Faculty of Ulyanovsk State Technical University in 2013. Defended his Candidate’s dissertation on the topic “Doubly stochastic autoregressive image models” in 2017. Research interests: pattern recognition, statistical analysis of images, computer vision, and mathematical modeling. Associate Professor at the Department of Data Analysis and Machine Learning at the Financial University under the Government of the Russian Federation.

Vitaliy Evgenevich Dementev, Doctor of Technical Sciences, Associate Professor. Born 1982. Graduated from Ulyanovsk State Technical University with a degree in applied mathematics in 2004. Defended his Candidate’s dissertation on the topic “Signal Detection in Multispectral Images” in 2007. He defended his Doctoral dissertation in 2020. Research interests: statistical analysis of random processes and fields. Head of the Telecommunications Department of Ulyanovsk State Technical University.

Konstantin Konstantinovich Vasiliev, Doctor of Technical Sciences, Professor. Born in 1948. Graduated from Leningrad Electrotechnical Institute with a degree in radioelectronic devices in 1972. Research interests: statistical analysis of random processes and fields. Defended his Candidate’s dissertation in 1975. He defended his Doctoral dissertation in 1985. Honored Worker of Science and Technology of the Russian Federation and Corresponding Member of the Academy of Sciences of the Republic of Tatarstan. Professor of the Department of Telecommunications, Ulyanovsk State Technical University.

Aleksandr Grigor’evich Tashlinskii, Doctor of Technical Sciences, Professor. Born in 1954. Graduated from Ulyanovsk Polytechnic Institute in 1977 with a degree in radio engineering. Research interests: statistical analysis of images, estimation of image parameters, and pseudogradient algorithms. Defended his Doctoral dissertation in 1999. Head of the Department of Radio Engineering, Ulyanovsk State Technical University.

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Andriyanov, N.A., Dementev, V.E., Vasiliev, K.K. et al. Investigation of Methods for Increasing the Efficiency of Convolutional Neural Networks in Identifying Tennis Players. Pattern Recognit. Image Anal. 31, 496–505 (2021).

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  • face recognition
  • convolutional neural networks
  • accuracy
  • precision
  • recall
  • probability of correct recognition
  • regularization
  • drop out
  • augmentation
  • doubly stochastic model
  • transfer learning
  • VGG-16
  • Kaggle
  • dogs vs. cats
  • Federer vs. Nadal