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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 10/2020

03.04.2020 | Original Article

Exploring of alternative representations of facial images for face recognition

verfasst von: Yongbin Qin, Lilei Sun, Yong Xu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 10/2020

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Abstract

Description and classification of face images is a significant task of computer vision, machine learning and pattern recognition communities. In the past, researchers have made tremendous efforts in this task. Previous researchers always seek high-resolution face images for better image classification. However, with this paper, we present and demonstrate a new opinion that in some cases the use of alternative representations of facial images are very useful for face recognition and properly reducing the image resolution might be beneficial to better classification of face images. This may be attributed to the deformable property of faces and the fact that the proposed alternative representations can in some extent reduce the within-class difference of facial images. Also, the presented idea appear to be useful for helping people to improve face recognition techniques in real worlds.
Vorheriger Artikel Gradient boosting in crowd ensembles for Q-learning using weight sharing
Nächster Artikel Bipolar fuzzy Petri nets for knowledge representation and acquisition considering non-cooperative behaviors

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Metadaten
Titel
Exploring of alternative representations of facial images for face recognition
verfasst von
Yongbin Qin
Lilei Sun
Yong Xu
Publikationsdatum
03.04.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 10/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01116-4

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