Face recognition method and device based on multispectral fusion
A face recognition and multi-spectral technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem of low accuracy and achieve the effect of reducing difficulty and speeding up speed and efficiency
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Embodiment 1
[0043] Such as figure 1 Shown, a kind of face recognition method based on multispectral fusion is characterized in that, comprises the following steps:
[0044] S1: collecting RGB images and infrared images;
[0045] S2: preprocessing the collected RGB image and infrared image;
[0046] S3: registering the preprocessed RGB image and the infrared image;
[0047] S4: Fusing the registered RGB image and the infrared image;
[0048] S5: Cut out the face from the fused image, and align the face images by detecting key points;
[0049] S6: Extract facial features from the aligned face images.
[0050] The local features of the facial image are extracted through the LBP (Local Binary Pattern) descriptor, which is robust to feature invariance, and the dimensionality of the extracted features is reduced. Finally, the features are compared with the face image repository to complete. face recognition.
Embodiment 2
[0052] On the basis of Example 1, the S1 step specifically includes:
[0053] S201: Perform grayscale image processing on the RGB image, and correct the infrared image;
[0054] S202: Perform WF de-illumination processing on the gray-scale processed RGB image, and perform histogram equalization processing on the corrected infrared image.
[0055] WF refers to the method of extracting light-invariant features from Weber face, which can effectively remove complex light effects.
[0056]
[0057] where A={-1,0,1}
[0058] The histogram equalization processing can improve the quality of the infrared image and highlight the features of the image.
Embodiment 3
[0060] On the basis of Example 1, the S3 step specifically includes:
[0061] S301: Obtain feature vectors of RGB image and infrared image feature points by feature extraction;
[0062] S302: Perform feature matching on the same-named points of the RGB image and the feature vector of the infrared image;
[0063] S303: Concatenate the same-named points to construct a transformation model, and obtain a registered image through affine transformation.
[0064] Even if the distance between the infrared camera and the ordinary camera is very close, the captured images still have a certain parallax. Before the fusion of the two images, the two images need to be registered and converted to the same viewing angle.
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