The target detection problem is defined as the need to separate targets from clutter instances. Among the many example-based techniques for the solution of this problem, the family of rejection-based classifiers are consistently exhibiting state-of-the-art accuracy while being the fastest. This rejection-based approach advocates the use of large set of weak-classifiers chained sequentially. After application of each such atom-block, a rejection of some of the clutter is performed while guaranteeing no loss of targets.
While intuitively appealing, theoretic background for this method was gathered only recently. Some roots of it can be traced to the boosting algorithm and the decision tree methods – two wide fields of research in machine learning that concentrate on using multiple weak-classifiers for the construction of a complicated overall machine. Rejection as a concept was proposed and analyzed by Nayar and Baker, with emphasis on the multi-class problems. More recently Elad, Hel-Or, and Keshet proposed the Maximal-rejection-Classifier (MRC), and employed it to the face detection problem. To conclude this list of works on the rejection-based idea, we should mention the work of Viola and Johns on the face detection problem using sub-linear weak-classifiers joined via boosting. In this talk I’ll survey the various contributions to the rejection idea and its efficient implementation on face detection problem.