| DOI | Resolve DOI: https://doi.org/10.1109/ICCAT.2013.6522006 |
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| Author | Search for: Salhi, A.I.; Search for: Kardouchi, M.; Search for: Belacel, N.1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | 2013 International Conference on Computer Applications Technology, ICCAT 2013, 20 January 2013 through 22 January 2013, Sousse |
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| Subject | Dimensional vectors; Feature dimensions; Feature vectors; HFOG; High dimensional feature; Histogram of oriented gradients (HOG); HOG; Oriented gradients; Computer applications; Fuzzy sets; Graphic methods; Statistical methods; Face recognition |
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| Abstract | Efficient face descriptors require a careful equilibration between accuracy and feature dimension. In recent years Histogram of Oriented Gradient (HOG) starts to be used in the face recognition task. However the best recognition rate for HOG requires a high dimensional feature. In this paper, we will incorporate fuzzy concept to HOG aiming to achieve a good recognition rate with a low feature vector dimension. The proposed Histogram of Fuzzy Oriented Gradient is applied to the face recognition task. Experimental results on ORL database have demonstrated that HFOG outperforms the original HOG with a lower dimensional vector. © 2013 IEEE. |
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| Publication date | 2013 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21269730 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | e488d603-ebe2-47c6-98c1-b0c84f31a721 |
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| Record created | 2013-12-13 |
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| Record modified | 2020-04-22 |
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