Illumination variation is a big problem in object recognition, which usually requires a costly compensation prior to classification. It would be desirable to have an image-to-image transform, which uncovers only the structure of an object for an efficient matching. In this context the contribution of our work is two-fold. First, we introduce illumination invariant local structure features for object detection. For an efficient computation we propose a modified census transform which enhances the original work of Zabih and Woodfill. We show some shortcomings and how to get over them with the modified version. S6econdly, we introduce an efficient four-stage classifier for rapid detection. Each single stage classifier is a linear classifier, which consists of a set of feature lookup-tables. We show that the first stage, which evaluates only 20 features filters out more than 99% of all background positions. Thus, the classifier structure is much simpler than previous described multi-stage approaches, while having similar capabilities. The combination of illumination invariant features together with a simple classifier leads to a real-time system on standard computers (60 msec, image size: 288/spl times/384, 2GHi Pentium). Detection results are presented on two commonly used databases in this field namely the MIT+CMU set of 130 images and the BioID set of 1526 images. We are achieving detection rates of more than 90% with a very low false positive rate of 10/sup -7/%. We also provide a demo program that can be found on the Internet http://www.iis.fraunhofer.de/bv/biometrie/download/.