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. This report describes research done within the Center for Biological & Computational Learning in the Department of Brain & Cognitive Sciences and in the Arti cial Intelligence Laboratory at the Massachusetts Institute of Technology. This research was sponsored by grants from: OÆce of Naval Research under contract No. N0001493-1-3085, OÆce of Naval Research (DARPA) under contract No. N00014-00-1-0907, National Science Foundation (ITR) under contract No. IIS-0085836, National Science Foundation (KDI) under contract No. DMS-9872936, and National Science Foundation under contract No. IIS-9800032. Additional support was provided by: Central Research Institute of Electric Power Industry, Center for eBusiness (MIT), Eastman Kodak Company, DaimlerChrysler AG, Compaq, Honda R&D Co., Ltd., Komatsu Ltd., Merrill-Lynch, NEC Fund, Nippon Telegraph & Telephone, Siemens Corporate Research, Inc., and The Whitaker Foundation.