Human face detection method

Abstract

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.

Description

technical field [0001] The invention relates to a face detection method. Background technique [0002] At present, face detection has been widely used in the new generation of human-machine interface, video surveillance and content-based retrieval and other fields. With the development of embedded technology and smart devices, the field of face detection applications has gradually emerged the requirements of mobility and outdoor work. However, the computing power of the embedded platform is limited. How to reduce the computing load of face detection at the algorithm level, reduce the number of memory accesses, and reduce storage space, so as to obtain a real-time embedded face detection system has become a top priority. [0003] Therefore, how to provide an effective face detection method with fast running speed and small memory usage has become an urgent problem to be solved by those skilled in the art. Contents of the invention [0004] The purpose of the present inven...

AI Technical Summary

Problems solved by technology

However, the computing power of the embedded platform is limited. How to reduce the computing load of face detection at the algorithm level, reduce the number of memory accesses, and reduce storage space, so as to obtain a real-time embedded face detection system has become a top priority.

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