Abstract
This paper presents initial experiments of an application of deep residual network to face recognition task. We utilize 50-layer deep neural network ResNet architecture, which was presented last year on CVPR2016. The neural network was modified and then fine-tuned for face recognition purposes. The method was trained and tested on challenging Casia-WebFace database and the results were benchmarked with a simple convolutional neural network. Our experiments of classification of closed and open subset show the great potential of residual learning for face recognition.
Keywords
- Face recognition
- Classification
- Neural networks
- Data augmentation
- Residual training
- Computer vision
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References
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Acknowledgments
This work is supported by grant of the University of West Bohemia, project No. SGS-2016-039, by Ministry of Education, Youth and Sports of Czech Republic, project No. LO1506, by Russian Foundation for Basic Research, projects No. 15-07-04415 and 16-37-60100, and by the Government of Russian, grant No. 074-U01. Moreover, access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Gruber, I., Hlaváč, M., Železný, M., Karpov, A. (2017). Facing Face Recognition with ResNet: Round One. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2017. Lecture Notes in Computer Science(), vol 10459. Springer, Cham. https://doi.org/10.1007/978-3-319-66471-2_8
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DOI: https://doi.org/10.1007/978-3-319-66471-2_8
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