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Robust Face Recognition for Data Mining
Lovell, Brian C. and Chen, Shaokang (2005). Robust Face Recognition for Data Mining. In Wang, John (Ed.), Encyclopedia of Data Warehousing and Mining (pp. 965-972) Hershey, USA: Idea Group.
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| Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials) |
| Name |
Description |
MIMEType |
Size |
Downloads |
Robust_Face_Reco.pdf
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Robust_Face_Reco.pdf |
application/pdf |
197.49KB |
837 |
| Author(s) |
Lovell, Brian C.
Chen, Shaokang
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| Title of chapter |
Robust Face Recognition for Data Mining
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| Title of book |
Encyclopedia of Data Warehousing and Mining
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| Place of Publication |
Hershey, USA
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| Publisher |
Idea Group
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| Publication year |
2005
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| Editor(s) |
Wang, John
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| ISBN |
1-59140-557-2
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| Volume number |
2
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| Start page |
965
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| End page |
972
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| Total pages |
8
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| Total chapters |
135
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| Collection year |
2005
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| Language |
eng
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| Subjects |
280103 Information Storage, Retrieval and Management
280208 Computer Vision
280203 Image Processing
280207 Pattern Recognition
B1
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| Abstract/Summary |
While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone.
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| Keyword(s) |
iris-research
data mining
face recognition
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