The human face comprises of just 0.01% positive (faces) sub windows on average. It is tedious to process negative sub windows; thus, time is focused on sub windows that are positive. To accomplish this, a 2-feature classifier can be utilized. The first classifier acts as a first line defense to help remove all negative sub windows, and second classifier can be used to remove the negatives that were more difficult to detect in the first classifier. Gradually, more complicated classifiers cascade usually achieves better rates of detection. In short, the AdaBoost algorithm constructs a “strong classifier” as a linear combination of weighted simple “weak classifiers”.

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