8. An apparatus for generating a strong classifier for face detection, the apparatus having a processor comprising: a determining module, configured to determine, according to a size of a prestored image training sample, a parameter of each of one or more weak classifiers of the image training sample; a processing module, configured to obtain, according to a preset threshold of each of the one or more weak classifiers and the parameter of each weak classifier, a sketch value of each of the one or more weak classifiers of the image training sample; a selecting module, configured to calculate, according to the sketch value of each weak classifier obtained by the processing module and an initial weight of the image training sample, a weighted classification error of each of the one or more weak classifiers of the image training sample; and to obtain, according to the weighted classification error of each of tweak classifier, one or more optimal weak classifiers at least one optimal weak classifier according to the weighted classification error; and a generating module, configured to generate a strong classifier for face detection according to the one or more optimal weak classifiers selected by the selecting module.
13. The apparatus according to claim 8 , wherein the selecting module is further configured to: calculate, according to the sketch value of the weak classifier and a feature identifier of each image training sample, a classification error of classification performed by the weak classifier on each image training sample; and obtain a weighted classification error of the weak classifier by performing weighted accumulation, according to an initial weight of each image training sample, on the classification error of classification performed by the weak classifier on each image training sample wherein the feature identifier of the image training sample is one of a preset input vector corresponding to a human face feature of the image training sample or a preset input vector corresponding to a non-human face feature of the image training sample.