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An Analysis of One-Shot Augmented Learning: A Face Recognition Case Study

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1410)

Abstract

Nowadays artificial intelligence models increase in complexity and people tend to doubt how these systems work and how vulnerable they are when they use these systems. The systems indeed use user data, nonetheless, it is possible to reduce this data with techniques like one-shot learning. This study aims to compare how one-shot learning works in face recognition compared to the one-shot augmented learning that uses data augmentation techniques. The research shows that in many face recognition models the data augmentation technique is highly effective. Also, the study allows us to determine the best face recognition model for low data training.

Keywords

  • Data augmentation
  • Ethic artificial intelligence
  • Face recognition
  • LFW dataset
  • One-shot augmented learning
  • One-shot learning

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Acknowledgments

This work was supported by the Spanish Agencia Estatal de Investigación. Project Monitoring and tracking systems for the improvement of intelligent mobility and behavior analysis (SiMoMIAC). PID2019-108883RB-C21/AEI/10.13039/501100011033. The research of Diego M. Jiménez-Bravo has been co-financed by the European Social Fund and Junta de Castilla y León (Operational Programme 2014–2020 for Castilla y León, EDU/574/2018 BOCYL). Álvaro Lozano research work is supported by a postdoctoral fellowship from the University of Salamanca and Banco Santander. André Filipe Sales Mendes’s research was co-financed by the European Social Fund and Junta de Castilla y León (Operational Programme 2014–2020 for Castilla y León, EDU/556/2019 BOCYL).

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Correspondence to Diego M. Jiménez-Bravo .

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Jiménez-Bravo, D.M., Murciego, Á.L., Mendes, A.S., Silva, L.A., Iglesia, D.H.D.L. (2022). An Analysis of One-Shot Augmented Learning: A Face Recognition Case Study. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_6

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