Provided is a face detecting method, first through training positive negative samples, a cascade classifier is obtained to detect whether each subarea corresponding to each eigenvalue does not belong to face component in the image according to the eigenvalue of the image, then the face image to be detected is converted to a face grey chart, which is furthermore converted to a face integral image, then the face integral image is divided into a plurality of sub-integral domains, and then the eigenvalue in the corresponding sub-domain of each sub-integral domain is computed according to the obtained cascade classifier, and based on the computed eigenvalue, the cascade classifier is adopted to detect step by step whether each sub-integral domain does not belong to the face component, to eliminate the sub-integral domains which do not belong to the face component, finally, face repeated are is processed combinedly to determine position and size of the face according to the judgement result. Due to floating point and fixed point operation adopted in the detecting process, detecting speed of the face is effectively advanced, meanwhile appropriative memory is reduced.