Facial semantic descriptors based on information granules

In this paper, we investigate a granular data description for facial components in which a characterization of facial components is presented by a collection of information granules. Firstly, the facial landmark detector is utilized to extract facial components automatically. Secondly, semantic conc...

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Main Authors: Ren, Y., Guan, W., Liu, Wan-Quan, Xi, J., Zhu, L.
Format: Journal Article
Published: Elsevier Inc 2019
Online Access:http://hdl.handle.net/20.500.11937/73860
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author Ren, Y.
Guan, W.
Liu, Wan-Quan
Xi, J.
Zhu, L.
author_facet Ren, Y.
Guan, W.
Liu, Wan-Quan
Xi, J.
Zhu, L.
author_sort Ren, Y.
building Curtin Institutional Repository
collection Online Access
description In this paper, we investigate a granular data description for facial components in which a characterization of facial components is presented by a collection of information granules. Firstly, the facial landmark detector is utilized to extract facial components automatically. Secondly, semantic concepts are formed by involving various mechanisms of fuzzy clustering based on these detected landmarks. A collection of numeric prototypes can be sought as a blueprint of the descriptors. Consequently, the information granules are being formed around the prototypes that are engaged by the fundamental ideas of Granular Computing, especially the principle of justifiable granularity. Multiple experiments on Multi-PIE facial database illustrate the proposed facial semantic descriptors based on information granules not only can characterize the key semantics of facial components of data, but also can improve the semantic classification performance in comparison with human perception.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T10:58:22Z
publishDate 2019
publisher Elsevier Inc
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spelling curtin-20.500.11937-738602019-08-22T03:41:49Z Facial semantic descriptors based on information granules Ren, Y. Guan, W. Liu, Wan-Quan Xi, J. Zhu, L. In this paper, we investigate a granular data description for facial components in which a characterization of facial components is presented by a collection of information granules. Firstly, the facial landmark detector is utilized to extract facial components automatically. Secondly, semantic concepts are formed by involving various mechanisms of fuzzy clustering based on these detected landmarks. A collection of numeric prototypes can be sought as a blueprint of the descriptors. Consequently, the information granules are being formed around the prototypes that are engaged by the fundamental ideas of Granular Computing, especially the principle of justifiable granularity. Multiple experiments on Multi-PIE facial database illustrate the proposed facial semantic descriptors based on information granules not only can characterize the key semantics of facial components of data, but also can improve the semantic classification performance in comparison with human perception. 2019 Journal Article http://hdl.handle.net/20.500.11937/73860 10.1016/j.ins.2018.11.056 Elsevier Inc restricted
spellingShingle Ren, Y.
Guan, W.
Liu, Wan-Quan
Xi, J.
Zhu, L.
Facial semantic descriptors based on information granules
title Facial semantic descriptors based on information granules
title_full Facial semantic descriptors based on information granules
title_fullStr Facial semantic descriptors based on information granules
title_full_unstemmed Facial semantic descriptors based on information granules
title_short Facial semantic descriptors based on information granules
title_sort facial semantic descriptors based on information granules
url http://hdl.handle.net/20.500.11937/73860