The Gentle Ada Boost Algorithm is a variant of the powerful boosting learning technique . It is used to select a set of simple CARTs to achieve a given detection and error rate. In the following, a detection is referred to as a hit and an error as a false alarm. The various Ada Boost algorithms differ in the update scheme of the weights. According to Lienhart et al., the Gentle Ada Boost Algorithm is currently the most successful learning procedure tested for face detection applications .
The learning is based on weighted training examples , where are the images and the classified output . At the beginning of the learning phase, the weights are initialized with . The following three steps are repeated to select simple CARTs until a given detection rate is reached:
The final output of the classifier is sign, with the weighted return value of the CART. Next, a cascade based on these classifiers is built.