TY - GEN
T1 - Average of synthetic exact filters
AU - Bolme, David S.
AU - Draper, Bruce A.
AU - Beveridge, J. Ross
PY - 2009
Y1 - 2009
N2 - This paper introduces a class of correlation filters called Average of Synthetic Exact Filters (ASEF). For ASEF, the correlation output is completely specified for each training image. This is in marked contrast to prior methods such as Synthetic Discriminant Functions (SDFs) which only specify a single output value per training image. Advantages of ASEF training include: insenitivity to over-fitting, greater flexibility with regard to training images, and more robust behavior in the presence of structured backgrounds. The theory and design of ASEF filters is presented using eye localization on the FERET database as an example task. ASEF is compared to other popular correlation filters including SDF, MACE, OTF, and UMACE, and with other eye localization methods including Gabor Jets and the OpenCV Cascade Classifier. ASEF is shown to outperform all these methods, locating the eye to within the radius of the iris approximately 98.5% of the time.
AB - This paper introduces a class of correlation filters called Average of Synthetic Exact Filters (ASEF). For ASEF, the correlation output is completely specified for each training image. This is in marked contrast to prior methods such as Synthetic Discriminant Functions (SDFs) which only specify a single output value per training image. Advantages of ASEF training include: insenitivity to over-fitting, greater flexibility with regard to training images, and more robust behavior in the presence of structured backgrounds. The theory and design of ASEF filters is presented using eye localization on the FERET database as an example task. ASEF is compared to other popular correlation filters including SDF, MACE, OTF, and UMACE, and with other eye localization methods including Gabor Jets and the OpenCV Cascade Classifier. ASEF is shown to outperform all these methods, locating the eye to within the radius of the iris approximately 98.5% of the time.
UR - http://www.scopus.com/inward/record.url?scp=70450210839&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2009.5206701
DO - 10.1109/CVPRW.2009.5206701
M3 - Conference contribution
AN - SCOPUS:70450210839
SN - 9781424439935
T3 - 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
SP - 2105
EP - 2112
BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
PB - IEEE Computer Society
T2 - 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Y2 - 20 June 2009 through 25 June 2009
ER -