Abstract
In this paper, we propose a benchmark of shoe recognition based on convolutional neural network. To meet the training and testing needs, we also set up a shoe database which contains 50 pairs of shoes and 160231 images. The Caffe framework is applied in combination with different network models to train and test the image data of shoes, which could obtain the best network model, and the similarity measurement between different shoe pictures is estimated for shoe verification. At the same time, the error recognition image analysis and robustness test are performed. The experimental results show that the proposed method achieves good performance with an accuracy of 95.31%. The proposed method provides a new way for shoe recognition.
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Acknowledgements
This work is supported by National Key Research and Development Program of China (Grant No. 2017YFC0822003), the Fundamental Research Funds for the Central Universities of China (Grant No. 2018JKF217).
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Yang, M., Jiang, H., Tang, Y. (2019). Shoe Pattern Recognition: A Benchmark. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_45
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DOI: https://doi.org/10.1007/978-3-030-31456-9_45
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