Abstract
Face recognition systems are of great interest in many applications. We present some results from a comparison on different classification methods using an open source tool that works with Convolutional Neural Networks to extract facial features. This work focuses on the performance obtainable from a multi-class classifier, trained with a reduced number images, to identify a person between a group of known and unknown subjects . The overall system has been implemented in an Odroid XU-4 Platform.
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University of Trieste—FRA projects.
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Marsi, S. et al. (2019). A Face Recognition System Using Off-the-Shelf Feature Extractors and an Ad-Hoc Classifier. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018. Lecture Notes in Electrical Engineering, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-030-11973-7_18
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DOI: https://doi.org/10.1007/978-3-030-11973-7_18
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