| dc.contributor.author | Pan, Z. | |
| dc.contributor.author | Rust, A.G. | |
| dc.contributor.author | Bolouri, H. | |
| dc.date.accessioned | 2009-10-21T09:02:00Z | |
| dc.date.available | 2009-10-21T09:02:00Z | |
| dc.date.issued | 2000 | |
| dc.identifier.citation | Pan , Z , Rust , A & Bolouri , H 2000 , ' Image redundancy reduction for neural network classification using discrete cosine transforms ' , IJCNN , vol 2000 , pp. 149-154 . | en |
| dc.identifier.other | PURE: 95463 | |
| dc.identifier.other | dspace: 2299/3979 | |
| dc.identifier.uri | http://hdl.handle.net/2299/3979 | |
| dc.description | “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/IJCNN.2000.861296 | en |
| dc.description.abstract | High information redundancy and strong correlations in face images result in inefficiencies when such images are used directly in recognition tasks. In this paper, discrete cosine transforms (DCT) are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features, such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, high recognition rates can be achieved using only a small proportion (0.19%) of available transform components. This makes DCT-based face recognition more than two orders of magnitude faster than other approaches. | en |
| dc.language.iso | eng | |
| dc.relation.ispartof | IJCNN | en |
| dc.subject | backpropagation | en |
| dc.subject | face recognition | en |
| dc.title | Image redundancy reduction for neural network classification using discrete cosine transforms | en |
| dc.type | Article | en |
| dc.type | text | en |
| dc.contributor.institution | School of Computer Science | en |
| dc.identifier.doi | http://dx.doi.org/10.1109/IJCNN.2000.861296 | |
| dc.description.version | other | en |
| dc.description.status | Peer reviewed | en |
| herts.preservation.rarelyaccessed | true | |
