Human face recognition method

Abstract

The invention discloses a human face recognition method. the method comprises steps: (1) a data set is built for each pixel of a human face image, wherein the data set is composed of the gray value of the pixel and gray values of adjacent 8 pixels; (2) by using the adjacent 8 pixel gray values and in combination with the maximum likelihood estimation method, standard deviation parameters in a Gauss density function with the pixel gray value as a mean value are estimated; (3) the ratio of the standard deviation estimation value to the pixel gray value is calculated, an arc-tangent function is used for converting the ratio, and the converted value is the pixel illumination invariant feature; (4) all pixels of the human face are traversed to obtain a human face feature image based on the local standard deviation illumination invariant feature; (5) feature extraction is carried out on the human face feature image; and (6) the extracted features are classified to complete human face recognition. The method of the invention can overcome influences on the human face recognition rate by illumination changes, and has the advantages of low algorithm complexity, strong feature extraction ability and the like.

Description

technical field [0001] The invention relates to the field of face recognition research, in particular to a face recognition method. Background technique [0002] As a non-contact and friendly biometric identification technology, face recognition is one of the most basic and important functions of the human visual system. Although face recognition technology has developed rapidly in the past few decades, the There are still many challenging problems to be solved under uncontrollable conditions, such as changes in facial expressions, ages, scenes, etc. Among them, the change of lighting conditions is the most frequent, so it is of great significance to solve the influence of complex lighting on face recognition. [0003] In recent years, a series of methods to solve the problem of face recognition illumination changes have been proposed. These methods can be divided into four categories. The first category is to use traditional image processing methods to normalize the illu...

AI Technical Summary

Problems solved by technology

The second type is to establish a face illumination model, such as using spherical harmonics to represent illumination changes, etc., but this type of method requires a large number of training images, making this method unsuitable for actual face recognition systems

[0004] The methods mentioned above have solved the impact of variable lighting on the accuracy of face recognition to varying degrees, but they are still not ideal.

Images