Face Detection using Local SMQT Features and Split up Snow Classifier
The purpose of this paper is threefold: firstly, the local successive mean quantization transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up sparse network of winnows is presented to speed up the original classifier. Finally, t...
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| creator | Nilsson, M. Nordberg, J. Claesson, I. |
| description | The purpose of this paper is threefold: firstly, the local successive mean quantization transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up sparse network of winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the receiver operation characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors. A Matlab version of the face detection algorithm can be downloaded from http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13701& objectType=FILE. |
| doi_str_mv | 10.1109/ICASSP.2007.366304 |
| format | Conference Proceeding |
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Secondly, a split up sparse network of winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the receiver operation characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors. 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Secondly, a split up sparse network of winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the receiver operation characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors. A Matlab version of the face detection algorithm can be downloaded from http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13701& objectType=FILE.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2007.366304</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, , Vol.2, p.II-589-II-592 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_4217477 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biosensors Computer languages Detectors Face detection Image processing Lighting Object detection Object recognition Pattern recognition Quantization Sensor phenomena and characterization Snow Spatial databases |
| title | Face Detection using Local SMQT Features and Split up Snow Classifier |
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