36. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for updating a classifier, the method comprising: receiving a sample; assigning a first importance weight to the sample based On a count of samples used to update the classifier; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, an updated error rate of the weak learner based on the outcome of classification and the first importance weight; selecting a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner.
36. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for updating a classifier, the method comprising: receiving a sample; assigning a first importance weight to the sample based On a count of samples used to update the classifier; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, an updated error rate of the weak learner based on the outcome of classification and the first importance weight; selecting a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner. 41. The non-transitory computer-readable medium of claim 36 , wherein each weak learner in the first plurality is based on a different Haar feature.