NDLI: Face Detection Algorithm and Feature Performance on FRGC 2.0 Imagery

Content Provider	IEEE Xplore Digital Library
Author	Beveridge, J.R. Alvarez, A. Saraf, J. Fisher, W. Flynn, P.J. Gentile, J.
Copyright Year	2007
Description	Author affiliation: Colorado State Univ., Fort Collins (Beveridge, J.R.; Alvarez, A.; Saraf, J.; Fisher, W.)
Abstract	The performance of three well known face detection algorithms and four alternative types of features are characterized using face data from the Face Recognition Grand Challenge. The three algorithms are a semi-naive Bayesian classifier, a neural network called a SNoW, and a cascade classifier using Haar wavelets. For the first two algorithms, ROC analysis is used to assess the relative value of wavelet features compared to simpler pixel features. No universally best feature is observed, and for imagery acquired under uncontrolled lighting, pixels perform slightly better than wavelets. The cascade classifier is found to be impossible to train in the same fashion as the other algorithms, but it is also found to perform very well using a training configuration supplied along with the algorithm as part of the OpenCV library.
Starting Page	1
Ending Page	7
File Size	2268007
Page Count	7
File Format	PDF
ISBN	9781424415960
DOI	10.1109/BTAS.2007.4401950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computer vision Face recognition Bayesian methods Snow Neural networks Wavelet analysis Libraries Face detection Pixel
Content Type	Text
Resource Type	Article

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