Abstract
Accurate face recognition is vital in person identification tasks and may serve as an auxiliary tool to opportunistic video shooting using Unmanned Aerial Vehicles (UAVs). However, face recognition methods often require complex Machine Learning algorithms to be effective, making them inefficient for direct utilization in UAVs and other machines with low computational resources. In this paper, we propose a method of training Autoencoders (AEs) where the low-dimensional representation is learned in a way such that the various classes are more easily discriminated. Results on the ORL and Yale datasets indicate that the proposed AEs are capable of producing low-dimensional representations with enough discriminative ability such that the face recognition accuracy achieved by simple, lightweight classifiers surpasses even that achieved by more complex models.
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Acknowledgments
This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 731667 (MULTIDRONE). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.
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Nousi, P., Tefas, A. (2017). Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_18
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DOI: https://doi.org/10.1007/978-3-319-65172-9_18
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