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A face feature extraction method with illumination robustness

A technology of illumination robustness and facial features, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as complex reflection models, slow processing speed, and high complexity of 3D algorithms, and achieve simplified calculations and processing, increase the recognition speed, and calculate the effect of large amount of data

Inactive Publication Date: 2008-05-28
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

However, it is more complicated to look for models such as light generation and reflection, and the requirements for equipment are relatively high, such as infrared devices, so this method is not flexible and the cost is relatively high.
Therefore, the second type of method is usually used, but the 3D algorithm complexity is relatively high, and the processing speed is relatively slow, which does not meet the requirements of real-time applications.

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  • A face feature extraction method with illumination robustness
  • A face feature extraction method with illumination robustness
  • A face feature extraction method with illumination robustness

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Embodiment 1

[0034] Fig. 1 shows that the first specific embodiment of the present invention is: an illumination robust face feature extraction method based on discrete Fourier transform phase reconstruction, including the following steps a to e:

[0035] Step a, preprocessing of face images:

[0036] For the original two-dimensional face image I with the size of M rows and N columns P Apply the discrete Fourier transform to reconstruct the phase information only to obtain the binarized face preprocessing image I with the size of M rows and N columns 预 P . The specific method is as follows:

[0037] For the original two-dimensional face image I with the size of M rows and N columns P Do discrete Fourier transform, in this case M=N=128 promptly to the original two-dimensional face image I of 128 rows and 128 column sizes P . The formula of the one-dimensional discrete Fourier transform is:,

[0038] y k = Σ ...

Embodiment 2

[0067] This example is basically the same as embodiment one, and the difference is only: obtaining n global human face feature vectors I in the d step fea P , (P=1, 2, 3, ... n), first carry out dimension reduction processing with linear discriminant analysis (LDA), obtain n global face feature vectors I after dimension reduction n-fea P , (P=1, 2, 3,...n), then construct and form the face feature database; the Euclidean distance of the corresponding E step ED ( I fea 0 , I fea P ) = Σ q = 1 2 L ( EN q 0 - ...

Embodiment 3

[0080] The third specific implementation of the present invention is: an illumination robust face feature extraction method based on edge information, which is basically the same as the first embodiment, except that the preprocessing of the face image in step a is different, and the preprocessing The method is: for the original two-dimensional face image I of the size of M rows and N columns P Use the edge detection algorithm to extract the contour features of the face, and then perform binarization on the face contour image to obtain a face preprocessing image I with a size of M rows and N columns after binarization 预 p . In this example, the original two-dimensional face image I P The sobel edge detection algorithm is used to extract the contour features of the face. Its more specific operation instructions are as follows:

[0081] 1. Preprocessing of face images (extracting edge information):

[0082] 1. For the original two-dimensional face image I with M rows and N c...

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Abstract

A facial feature extraction method with light robustness includes the following procedures: pre-treatment is conducted on a human face image by utilizing edge detection or discrete fourier transformation; after uniform non-overlapping blocking of the pretreated human face image, the row and the column variance projection entropies of each block are calculated and the row and the column variance projection entropies of each block are integrated into human facial feature vectors to construct a feature database of a plurality of human face images. In recognition authentication, according to the same methods of pretreatment and facial feature vector extraction, facial feature vectors to be recognized are computed and distance classification is performed on the computed vectors and facial image feature vectors in the feature database one by one to get the recognition results. The invention has the advantages that complicated lighting models and imaging devices are not needed; the method has excellent robustness to lighting and can extract features needed for face recognition with a high recognition rate even for facial images formed under poor lighting conditions; the method has fast processing speed and the ability of real-time treatment.

Description

technical field [0001] The invention relates to a biometric feature automatic recognition technology, in particular to a feature extraction method for face recognition under changing illumination conditions. Background technique [0002] In recent years, with the rapid development of biometric recognition technology, face recognition technology has attracted more and more attention from researchers. This technology has huge application advantages in video surveillance, human-computer interaction, and identity authentication. At present, there are many studies on face recognition under controlled conditions, and the results are relatively good. However, under illumination changes, the effect of face recognition drops sharply, and this problem has not been effectively solved yet. Generally, there are several processing methods for face recognition under lighting conditions: [0003] (1) Physical preprocessing of light [0004] Since there are many physical mathematical mod...

Claims

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Application Information

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IPC IPC(8): G06K9/00
Inventor 张家树陈存建
Owner SOUTHWEST JIAOTONG UNIV
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