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Face Detection using Local SMQT Features and Split Up SNoW Classifier
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2007 (English)Conference paper (Refereed) Published
Abstract [en]

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 bjectType=FILE

Place, publisher, year, edition, pages
Honolulu, 2007.
National Category
Signal Processing
URN: urn:nbn:se:bth-9269ISI: 000248908100148Local ID: diva2:837051
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Available from: 2012-09-18 Created: 2007-05-02 Last updated: 2015-06-30Bibliographically approved

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fulltext(127 kB)68 downloads
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Nilsson, MikaelClaesson, Ingvar
Signal Processing

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