A method for generating a strong classifier for face detection, comprising: determining, according to a size of a prestored image training sample, a parameter of at least one weak classifier of the image training sample, said parameter comprising a location and a size of a rectangular frame of a weak classifier; obtaining a multi-block local binary pattern of each of the weak classifiers of the image training sample according to a preset threshold of a weak classifier and the parameter of each of the weak classifiers; calculating a weighted classification error of each of the weak classifiers according to the multi-block local binary pattern and an initial weight of the image training sample, and obtaining at least one optimal weak classifier according to the weighted classification error, wherein the optimal weak classifier is a weak classifier that has a smallest weighted classification error among all the weak classifiers; and generating a strong classifier for face detection according to the optimal weak classifiers, characterized in that the obtaining of a multi-block local binary pattern comprises: for one weak classifier in all the weak classifiers, dividing an area, which uses the location of the rectangular frame of the weak classifier as a center and has a same size as the rectangular frame, into at least two areas, and calculating an average grayscale value of each of the areas; selecting a reference area from all the areas, and setting a sign bit of each of the areas except the reference area to the preset sign bit of the weak classifier, wherein the sign bit includes +1 or -1; obtaining a multi-block local binary pattern of the weak classifier according to the average grayscale values of the areas, except the reference area, in all the areas, the sign bit, and the preset threshold of a weak classifier; and repeating the foregoing steps until the multi-block local binary pattern of each of the weak classifiers in all the weak classifiers is obtained, and wherein the obtaining of a multi-block local binary pattern further comprises obtaining said multi-block local binary patterns for all preset thresholds and both possible states of the sign bit of the weak classifiers. determining, according to a size of a prestored image training sample, a parameter of at least one weak classifier of the image training sample, said parameter comprising a location and a size of a rectangular frame of a weak classifier; obtaining a multi-block local binary pattern of each of the weak classifiers of the image training sample according to a preset threshold of a weak classifier and the parameter of each of the weak classifiers; calculating a weighted classification error of each of the weak classifiers according to the multi-block local binary pattern and an initial weight of the image training sample, and obtaining at least one optimal weak classifier according to the weighted classification error, wherein the optimal weak classifier is a weak classifier that has a smallest weighted classification error among all the weak classifiers; and generating a strong classifier for face detection according to the optimal weak classifiers, for one weak classifier in all the weak classifiers, dividing an area, which uses the location of the rectangular frame of the weak classifier as a center and has a same size as the rectangular frame, into at least two areas, and calculating an average grayscale value of each of the areas; selecting a reference area from all the areas, and setting a sign bit of each of the areas except the reference area to the preset sign bit of the weak classifier, wherein the sign bit includes +1 or -1; obtaining a multi-block local binary pattern of the weak classifier according to the average grayscale values of the areas, except the reference area, in all the areas, the sign bit, and the preset threshold of a weak classifier; and repeating the foregoing steps until the multi-block local binary pattern of each of the weak classifiers in all the weak classifiers is obtained, and