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A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps
In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Loc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239878/ https://www.ncbi.nlm.nih.gov/pubmed/25333290 http://dx.doi.org/10.3390/s141019561 |
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author | Yin, Shouyi Dai, Xu Ouyang, Peng Liu, Leibo Wei, Shaojun |
author_facet | Yin, Shouyi Dai, Xu Ouyang, Peng Liu, Leibo Wei, Shaojun |
author_sort | Yin, Shouyi |
collection | PubMed |
description | In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. |
format | Online Article Text |
id | pubmed-4239878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-42398782014-11-21 A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps Yin, Shouyi Dai, Xu Ouyang, Peng Liu, Leibo Wei, Shaojun Sensors (Basel) Article In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. MDPI 2014-10-20 /pmc/articles/PMC4239878/ /pubmed/25333290 http://dx.doi.org/10.3390/s141019561 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yin, Shouyi Dai, Xu Ouyang, Peng Liu, Leibo Wei, Shaojun A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps |
title | A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps |
title_full | A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps |
title_fullStr | A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps |
title_full_unstemmed | A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps |
title_short | A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps |
title_sort | multi-modal face recognition method using complete local derivative patterns and depth maps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239878/ https://www.ncbi.nlm.nih.gov/pubmed/25333290 http://dx.doi.org/10.3390/s141019561 |
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