Face Detection using Local SMQT Features and Split Up SNoW Classifier

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Mikael Nilsson, Jörgen Nordberg, Ingvar Claesson
Title: Face Detection using Local SMQT Features and Split Up SNoW Classifier
Conference name: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Year: 2007
City: Honolulu
ISI number: 000248908100148
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Signal Processing (Sektionen för teknik – avd. för signalbehandling)
School of Engineering S- 372 25 Ronneby
+46 455 38 50 00
Authors e-mail: mikael.nilsson@bth.se
Language: English
Abstract: 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
Subject: Signal Processing\Detection and Classification
Signal Processing\General