<p><b>Abstract</b>—This paper offers a novel detection method, which works well even in the case of a complicated image collection—for instance, a frontal face under a large class of linear transformations. It is also successfully applied to detect 3D objects under different views. Call the collection of images, which should be detected, a <it>multitemplate</it>. The detection problem is solved by sequentially applying very simple filters (or <it>detectors</it>), which are designed to yield <it>small</it> results on the multitemplate (hence, “antifaces”), and <it>large</it> results on “random” natural images. This is achieved by making use of a simple probabilistic assumption on the distribution of natural images, which is borne out well in practice. Only images which passed the threshold test imposed by the first detector are examined by the second detector, etc. The detectors are designed to act independently so that their false alarms are uncorrelated; this results in a false alarm rate which decreases exponentially in the number of detectors. This, in turn, leads to a very fast detection algorithm. Typically, <tmath>$(1+\delta)N$</tmath> operations are required to classify an <it>N</it>-pixel image, where <tmath>$\delta< 0.5$</tmath>. Also, the algorithm requires no training loop. The algorithm's performance compares favorably to the well-known eigenface and support vector machine based algorithms, but is substantially faster.</p>