We introduce a family of multiscale, orientation-selective, non-parametric features ("ranklets") modelled on Haar wavelets. We clarify their relation to the Wilcoxon rank-sum test and the rank transform and provide an efficient scheme for computation based on the Mann-Whitney statistics. Finally, we show that ranklets outperform other rank features, Haar wavelets, SNoW and linear SVMs (based on independently published results) in face detection experiments over the 24'045 test images in the MIT-CBCL database.
['Fabrizio Smeraldi']
Ranklets: orientation selective non-parametric features applied to face detection