Face recognition method based on adaptive weighting and local characteristic fusion
An adaptive weighting and local feature technology, applied in the field of image processing, can solve the problems of lower face recognition rate and weak feature identification ability, achieve good recognition effect, improve recognition reliability, and suppress noise interference
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Embodiment 1
[0056] Such as Figure 1-3 shown.
[0057] A face recognition method based on adaptive weighted local feature fusion, comprising the following steps:
[0058] (1) Divide the selected face database into training samples and test samples;
[0059] (2) Perform geometric clipping and gamma correction preprocessing on the training samples and test samples in sequence;
[0060] (3) the image after the step (2) pretreatment is divided into m equal and non-overlapping sub-blocks of size, calculates the information entropy of each sub-block, obtains the weighting coefficient of each sub-block according to the information entropy; m=3× 3;
[0061] (4) Use the local binary mode to extract the texture features of the preprocessed image in step (2); note that any point in the image is (x, y), in a ring with the pixel point (x, y) as the center and R as the radius There are p sampling points uniformly distributed on the neighborhood, and the pixel gray value g of the pixel point (x, y) ...
Embodiment 2
[0066] The face recognition method based on adaptive weighted local feature fusion as described in Embodiment 1, the difference is that the steps are as follows:
[0067] When processing the image in step (2), first cut the sample geometrically into a picture with a size of 100 pixels × 100 pixels, and use gamma correction to adjust the contrast of the image. The realization formula is as follows:
[0068] I(x,y)=I(x,y) gamma .
[0069] Use gamma correction to adjust the contrast of the image to reduce the impact of illumination changes on the face image and suppress the interference of noise.
Embodiment 3
[0071] The face recognition method based on adaptive weighted local feature fusion described in Embodiment 2, the difference is that the gamma value is 0.5.
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