DocumentCode :
3209203
Title :
Self-normalized linear tests
Author :
Gangputra, S. ; Geman, Donald
Author_Institution :
Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA
Volume :
2
fYear :
2004
fDate :
27 June-2 July 2004
Abstract :
Making decisions based on a linear combination L of features is of course very common in pattern recognition. For distinguishing between two hypotheses or classes, the test is of the form sign (L - τ) for some threshold τ. Due mainly to fixing τ, such tests are sensitive to changes in illumination and other variations in imaging conditions. We propose a special case, a "self-normalized linear test" (SNLT), hard-wired to be of the form sign (L1 - L2) with unit weights. The basic idea is to "normalize" L1, which involves the usual discriminating features, by L2, which is composed of non-discriminating features. For a rich variety of features (e.g., based directly on intensity differences), SNLTs are largely invariant to illumination and robust to unexpected background variations. Experiments in face detection are promising: they confirm the expected invariances and out-perform some previous results in a hierarchical framework.
Keywords :
face recognition; feature extraction; lighting; pattern classification; face detection; illumination; nondiscriminating features; pattern classification; pattern recognition; self-normalized linear tests; Automatic testing; Degradation; Face detection; Lighting; Linear discriminant analysis; Mathematics; Object detection; Pattern recognition; Robustness; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2158-4
Type :
conf
DOI :
10.1109/CVPR.2004.1315221
Filename :
1315221
Link To Document :
بازگشت