Optimized recognition pretreatment method for human face

Abstract

The invention relates to an optimized human face recognition preprocessing method. The method comprises the following steps: firstly, converting a color human face image from a camera into a gray level image, then performing a scale normalization processing on the gray level image to cause the human face images to have the same size and posture, dividing the human face images into low-frequency components and high-frequency components by wavelet transformation, performing a histogram equalization processing on the low-frequency components only, executing wavelet reconstruction on the processed low-frequency components and the high-frequency components, and finally processing the reconstructed images by optimized median filtering. The method has the advantages of regulating the gray level range of the human face images, enhancing the contrast, better improving the human face gray level images with higher brightness, and enhancing the human face identification efficiency in a complicated illumination environment with different postures.

Description

technical field [0001] This patent relates to a face recognition preprocessing method, in particular to a traditional face recognition preprocessing method based on wavelet frequency division and optimization combined with face recognition preprocessing. Background technique [0002] With the development of society and the advancement of science and technology, the research of face recognition has attracted more and more attention. It plays a potential role in the application fields of identity verification, access control, security detection and monitoring, human-computer intelligent interaction and so on. The face recognition system is a complex system, in which most of the processed objects are face image data, these images include dynamic video sequence images and static face and scene images. The face image acquisition process is affected by environmental factors such as background, posture, and illumination, which will cause face image information to be inherently diff...

AI Technical Summary

Problems solved by technology

The change of ambient light is one of the main factors affecting the accuracy of portrait recognition, so in the implementation method of the laboratory environment, when the ambient light changes, the traditional test method will become unusable

Obviously, if the adverse effects of environmental factors on recognition cannot be reduced, it will be difficult to further improve the accuracy of face recognition.

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