An Empirical Comparison of SNoW and SVMs for Face Detection
Impressive claims have been made for the performance of the SNoW algorithm on face detection tasks by Yang et. al. [7]. In particular, by looking at both their results and those of Heisele et. al. [3], one could infer that the SNoW system performed substantially better than an SVM-based system, even when the SVM used a polynomial kernel and the SNoW system used a particularly simplistic \primitive" linear representation. We evaluated the two approaches in a controlled experiment, looking directly at performance on a simple, xed-sized test set, isolating out \infrastructure" issues related to detecting faces at various scales in large images. We found that SNoW performed about as well as linear SVMs, and substantially worse than polynomial SVMs. Copyright c Massachusetts Institute of Technology, 2001 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Arti cial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by a grant from the OÆce of Naval Research under contract No. N00014-93-13085. Additional support is provided by Eastman Kodak Company, Daimler-Benz AG, Siemens Corporate Research Inc., AT&T, Digital Equipment and Central Research Institute of Electric Power Industry.