A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms
dc.contributor.author | Al-Waisy, Alaa S. | * |
dc.contributor.author | Qahwaji, Rami S.R. | * |
dc.contributor.author | Ipson, Stanley S. | * |
dc.contributor.author | Al-Fahdawi, Shumoos | * |
dc.date.accessioned | 2018-10-11T11:10:23Z | |
dc.date.available | 2018-10-11T11:10:23Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Al-Waisy AS, Qahwaji R, Ipson S and Al-Fahdawi S (2015) A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 26-28 Oct. IEEE. pp 548-555. | en_US |
dc.identifier.uri | http://hdl.handle.net/10454/16600 | |
dc.description | yes | en_US |
dc.description.abstract | n this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches. | en_US |
dc.language.iso | en | en_US |
dc.rights | © 2015 IEEE. Reproduced in accordance with the publisher's self-archiving policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.subject | Face recognition | en_US |
dc.subject | Curvelet transform | en_US |
dc.subject | Fractal dimension | en_US |
dc.subject | Fractional brownian motion | en_US |
dc.subject | SDUMLA-HMT iris database | en_US |
dc.subject | Faces96 database | en_US |
dc.subject | UMIST database | en_US |
dc.title | A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms | en_US |
dc.status.refereed | n/a | en_US |
dc.type | Conference paper | en_US |
dc.type.version | Accepted Manuscript | en_US |
dc.identifier.doi | https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.78 | |
refterms.dateFOA | 2018-10-11T11:10:23Z |