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EnglishPattern recognition system developers have looked in multiple directions over the years and designed a broad spectrum of methodologies for face identification and verification, both in 2D and 3D. These techniques rely on sound methods and experimentations, and currently give high to excellent recognition rates in terms of performance. Nonetheless, it seems that the most performing face recognition system, especially when familiar faces are involved, is still the human being, able to detect known faces in the wild, in presence of occlusions or extreme light contrast, caricatures, sketches, partial views, blurred images. This is one of the manifold reasons why the human visual system at eye and brain level and face perception techniques are currently being studied by neuroscientists and psychologists, with the aim to uncover the processes underneath the human vision. The purpose of this work is to review the current literature about perception foundations and related biologically-inspired methodologies for face recognition.
Face perception foundations for pattern recognition algorithms / Marcolin, Federica; Vezzetti, Enrico; Monaci, Maria Grazia. - In: NEUROCOMPUTING. - ISSN 0925-2312. - STAMPA. - 443(2021), pp. 302-319. [10.1016/j.neucom.2021.02.074]
| Titolo: | Face perception foundations for pattern recognition algorithms | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.neucom.2021.02.074 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 1-s2.0-S0925231221003301.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia | |
| reviewed manuscript, changes accepted.pdf | 2. Post-print / Author's Accepted Manuscript | Non Pubblico - Accesso privato/ristretto | Embargo: 20/03/2023 Richiedi una copia | |
| manuscript.pdf | 1. Preprint / submitted version [pre- review] | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2885718
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