CN106845500A - A kind of human face light invariant feature extraction method based on Sobel operators - Google Patents
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Abstract
The invention discloses a kind of human face light invariant feature extraction method based on Sobel operators, comprise the following steps：Using Sobel operators to facial image Edge contrast, the minutia of face horizontal direction is extracted；Division arithmetic is carried out with original image respective pixel, the human face light part of slow change is eliminated, acquired results are exactly the human face light invariant features for needing to extract.Illumination of the present invention to different angles has certain inhibitory action, and the face identification rate after treatment after the more existing algorithm process of the discrimination of facial image is high；The present invention does photo-irradiation treatment to facial image using Sobel operators and division arithmetic, and method is simple, and recognition speed is fast.
Description
Technical field
The present invention relates to mode identification technology, specifically a kind of human face light invariant features based on Sobel operators
Extracting method.
Background technology
In the epoch that current this digitlization is developed rapidly, efficiently identity discriminating is carried out so that it is guaranteed that various digitlizations are believed
The security of breath has become universal and important social concern.And the mode of identification has a lot, such as based on password, intelligence
The RMs such as energy card, password, biological characteristic.Living things feature recognition mode can be divided into fingerprint, vocal print, iris, retina, people again
Several specific RMs such as face feature, face recognition technology is widely used by the disguised most strong of shooting collection data, relates to
And multiple ambits such as pattern-recognition, image procossing, computer vision.Can be used to public security deploy to ensure effective monitoring and control of illegal activities monitoring, civil aviaton's safety check, port
Access and exit control, the discriminating of customs identity, intelligent entrance guard, the checking of driver's driving license and all kinds of bank cards, credit card, the holder of deposit card
Identity differentiate, the identity of social insurance differentiates etc..Even if the numerous advantages of face recognition technology are simultaneously deposited, but still there are many shadows
Ring the factor of recognition performance, such as attitude, age, illumination.Wherein, the uncertain and complicated and changeable property of photoenvironment is to face
The influence that recognition performance is produced is the most serious.The method for solving lighting issues in recognition of face is broadly divided into three classes：Extract illumination
Invariant features, the modeling of illumination variation, illumination condition standardization.In the method for extracting illumination invariant feature, it is broadly divided into again
Two kinds：The first is, in log-domain, to eliminate illumination component by LPF, for example：In log-domain, become using discrete cosine
Change (DCT) or wavelet transformation is filtered the methods such as treatment.Second is construction division arithmetic, eliminates slow by division arithmetic
The illumination component of change, for example：Gradient face (Gradient-face), weber face (Weber-face), local binary patterns
Methods such as (Local Binary Pattern, LBP).Therefore, illumination invariant feature how is efficiently extracted, new think of is opened up
Road, excavates new method, becomes one of basic problem of recognition of face.
The content of the invention
It is an object of the invention to provide a kind of human face light based on Sobel operators that can improve face identification rate not
Become feature extracting method, to solve the problems, such as to be proposed in above-mentioned background technology.
To achieve the above object, the present invention provides following technical scheme：
A kind of human face light invariant feature extraction method based on Sobel operators, comprises the following steps：
(1) minutia of face horizontal direction is extracted to facial image Edge contrast using Sobel operators；
(2) division arithmetic is carried out with original image respective pixel, the human face light part of slow change is eliminated, acquired results is exactly
Need the human face light invariant features for extracting.
As further scheme of the invention：According to illumination reflection model, any Gray Face image I (x, y) can see
Into the product for being reflecting component R (x, y) and illumination component L (x, y), i.e. I (x, y)=R (x, y) L (x, y)；The step (1)
In, during Edge contrast, only use Sobel operator horizontal direction upper die platesFace figure after being sharpened
As I'.
As further scheme of the invention：With the D of Sobel operatorsxMatrix template is sharpened the face figure after treatment
Picture, the gray value of certain pixel (x, y) is I'(x, y) as shown in formula (1)：
I'(x, y)=I (x-1, y-1)+2I (x, y-1)+I (x+1, y-1)-I (x-1, y+1) -2I (x, y+1)-I (x+1, y
+1) (1)；
In order to express easily, I (x+m, y+n) is abbreviated as Im,n, similarly, R (x+m, y+n) is abbreviated as Rm,n, L (x+m, y+n)
It is abbreviated as Lm,n, then formula (1) be abbreviated as formula (2), it is as follows：
I'0,0=I-1,-1+2I0,-1+I1,-1-I-1,1-2I0,1-I1,1(2)；
Formula (3) is further obtained according to illumination reflection model：
I'0,0=R-1,-1L-1,-1+2R0,-1L0,-1+R1,-1L1,-1-R-1,1L-1,1-2R0,1L0,1-R1,1L1,1 (3)。
As further scheme of the invention：Division arithmetic is in the step (2)R (x, y) represents the key of face
Information, and L (x, y) is slow change, is approximate constant in part, so the component is approximately eliminated by division arithmetic,
Obtain formula (4)
As further scheme of the invention：Illumination component L (x, y) change is slow, so there is formula (5)：
L0,0≈L-1,-1≈L0,-1≈L1,-1≈L-1,1≈L0,1≈L1,1(5)；
All variables for formula (5) use LtSubstitute, and substitute into formula (5), obtain formula (6)：
It is thus eliminated that illumination component.
Compared with prior art, the beneficial effects of the invention are as follows：
Illumination of the present invention to different angles has certain inhibitory action, and the discrimination of facial image is more existing after treatment
Algorithm process after face identification rate it is high；The present invention does photo-irradiation treatment using Sobel operators and division arithmetic to facial image,
Method is simple, and recognition speed is fast.
Brief description of the drawings
Fig. 1 is the basic flow sheet that the present invention extracts human face light invariant features.
Fig. 2 is the eight neighborhood gray scale value matrix in the present invention centered on (x, y) and its corresponding simplified expression schematic diagram.
Fig. 3 is to extract the face exemplary plot before and after CMU PIE face database illumination invariant features.
Fig. 4 is to extract the face exemplary plot before and after the illumination invariant feature of extension Yale B front faces storehouse.
Specific embodiment
The technical scheme of this patent is described in more detail with reference to specific embodiment.
Fig. 1-4 are referred to, a kind of human face light invariant feature extraction method based on Sobel operators is comprised the following steps：
(1) minutia of face horizontal direction is extracted to facial image Edge contrast using Sobel operators；
(2) division arithmetic is carried out with original image respective pixel, the human face light part of slow change is eliminated, acquired results is exactly
Need the human face light invariant features for extracting.
According to illumination reflection model, any Gray Face image I (x, y) can regard reflecting component R (x, y) and illumination as
The product of component L (x, y), i.e. I (x, y)=R (x, y) L (x, y)；In the step (1), during Edge contrast, only use
Sobel operator horizontal direction upper die platesFacial image I' after being sharpened.
Division arithmetic is in the step (2)R (x, y) represents the key message of face, and L (x, y) is slow change
, it is being locally approximate constant, so approximately eliminating the component by division arithmetic.
Specifically, the human face light invariant feature extraction method based on Sobel operators, comprises the following steps that：
(1) shown in the 8 neighborhoods such as Fig. 2 (a) centered on a certain pixel (x, y) of original image, stated to simplify, middle imago
Gray value I (x, y) at vegetarian refreshments (x, y) place is expressed as I0,0, such as shown in Fig. 2 (b), gray value I (x+m, y+n) table of its neighborhood point
It is I to statem,n。
According to illumination reflection model, any Gray Face image I (x, y) can regard reflecting component R (x, y) and illumination as
The product of component L (x, y), i.e. I (x, y)=R (x, y) L (x, y), wherein L (x, y) depend on light source and only represent facial illumination
Component, R (x, y) depends on the surface characteristics of object and contains the key message of face, in the present invention I (x+m, y+n)
R can be expressed asm,nLm,n。
Sobel operators can carry out Edge contrast horizontally and vertically to image, strengthen the details side of image
Edge and contour feature.Wherein, the template of horizontal direction minutia is extractedExtract vertical direction details special
The template levied
The present invention in following derivation, only with Sobel operators DxTemplate is sharpened treatment to facial image, obtains
To I'.Specific principle derivation is as follows：
If with Sobel operators DxTemplate is sharpened facial image I' pixel centers pixel (x, y) after treatment
Gray value is I'(x, y), such as shown in formula (1)：
I'(x, y)=I (x-1, y-1)+2I (x, y-1)+I (x+1, y-1)-I (x-1, y+1) -2I (x, y+1)-I (x+1, y
+1) (1)
In order to express easily, I (x+m, y+n) is abbreviated as Im,n, similarly, R (x+m, y+n) is abbreviated as Rm,n, L (x+m, y+n)
It is abbreviated as Lm,n, then formula (1) be abbreviated as formula (2), it is as follows：
I'0,0=I-1,-1+2I0,-1+I1,-1-I-1,1-2I0,1-I1,1 (2)
Formula (3) can further be obtained according to illumination reflection model：
I'0,0=R-1,-1L-1,-1+2R0,-1L0,-1+R1,-1L1,-1-R-1,1L-1,1-2R0,1L0,1-R1,1L1,1 (3)
(2) to the facial image I' of Edge contrast, can directly divided by original image (i.e.) eliminate approximate slow change
The illumination component of change.Specific formulation process is as follows：
Because illumination component L (x, y) change is slow, equal in Local approximation, so there is formula (5)：
L0,0≈L-1,-1≈L0,-1≈L1,-1≈L-1,1≈L0,1≈L1,1 (5)
All variables for formula (5) can use LtSubstitute, and substitute into formula (5), therefore obtain formula (6)：
Understood to have eliminated illumination component by formula (6).
The present invention is tested in CMU PIE and extension Yale B face databases：
1st, face database introduction is tested：CMU PIE face databases have 68 the 41368 of people facial images, the present invention
Selection is tested in front face light group (C27), and C27 light groups include 1428 face samples altogether.During experiment,
The facial size for using is 64 × 64, and an image is chosen as training sample from 21 kinds of different illumination of each face, its
Remaining face is used as test sample.
Altogether comprising 38 9 kinds of people different attitudes, every kind of attitude includes 64 kinds of different light to extension Yale B face databases again
According to situation.The present invention is only tested totally in extension Yale B front faces storehouse (2432 faces), and face images are pressed
Angle, θ according to incident illumination can be divided into 5 subsets：Subset 1 (12 ° of θ ＜) has 266 samples, subset 2 (13 ° of 25 ° of ＜ θ ＜) to have
456 samples, subset 3 (26 ° of 50 ° of ＜ θ ＜) have 456 samples, subset 4 (51 ° of 77 ° of ＜ θ ＜) to have 532 samples, (θ of subset 5
77 ° of ＞) there are 722 samples.During experiment, the facial size for using is 192 × 168, with subset 1 as training sample, its minor
Collection is used as test sample.
2nd, experimental result：
The human face light invariant features of extraction and original image have been carried out contrast and have found to be processed through the inventive method by experiment 1
Facial image feature afterwards becomes apparent, and effectively overcomes influence of the illumination to face characteristic.In CMU PIE front faces storehouse
With the face example difference before and after extraction illumination invariant feature in extension Yale B front faces storehouse as shown in Figure 3, Figure 4.
Experiment 2, the human face light invariant features that the inventive method will be extracted calculate contact transformation anyway and compress span
To (- pi/2, pi/2), using L1 norm measurement distances after, using nearest neighbor classifier discriminant classification.On CMU PIE face databases
Correct recognition rata be 96.47%, extension Yale B face databases face correct recognition rata be 99.35%, it is constant to face
Feature extraction has preferable robustness.
Illumination of the present invention to different angles has certain inhibitory action, and the discrimination of facial image is more existing after treatment
Algorithm process after face identification rate it is high；The present invention does photo-irradiation treatment using Sobel operators and division arithmetic to facial image,
Method is simple, and recognition speed is fast.
The better embodiment to this patent is explained in detail above, but this patent is not limited to above-mentioned embodiment party
Formula, in the ken that one skilled in the relevant art possesses, can also be on the premise of this patent objective not be departed from
Make a variety of changes.
Claims (5)
1. a kind of human face light invariant feature extraction method based on Sobel operators, it is characterised in that comprise the following steps：
(1) minutia of face horizontal direction is extracted to facial image Edge contrast using Sobel operators；
(2) division arithmetic is carried out with original image respective pixel, the human face light part of slow change is eliminated, acquired results is exactly to need
The human face light invariant features of extraction.
2. the human face light invariant feature extraction method based on Sobel operators according to claim 1, it is characterised in that
According to illumination reflection model, any Gray Face image I (x, y) is multiplying for reflecting component R (x, y) and illumination component L (x, y)
Product, i.e. I (x, y)=R (x, y) L (x, y)；In the step (1), during Edge contrast, Sobel operator horizontal directions have only been used
Upper die plateFacial image I' after being sharpened.
3. the human face light invariant feature extraction method based on Sobel operators according to claim 1-2, its feature exists
In with the D of Sobel operatorsxMatrix template is sharpened the facial image after treatment, and the gray value of certain pixel (x, y) is I'
(x, y) is such as shown in formula (1)：
I'(x, y)=I (x-1, y-1)+2I (x, y-1)+I (x+1, y-1)-I (x-1, y+1) -2I (x, y+1)-I (x+1, y+1) (1)；
In order to express easily, I (x+m, y+n) is abbreviated as Im,n, similarly, R (x+m, y+n) is abbreviated as Rm,n, L (x+m, y+n) brief notes
It is Lm,n, then formula (1) be abbreviated as formula (2), it is as follows：
I'0,0=I-1,-1+2I0,-1+I1,-1-I-1,1-2I0,1-I1,1(2)；
Formula (3) is further obtained according to illumination reflection model：
I'0,0=R-1,-1L-1,-1+2R0,-1L0,-1+R1,-1L1,-1-R-1,1L-1,1-2R0,1L0,1-R1,1L1,1 (3)。
4. the human face light invariant feature extraction method based on Sobel operators according to claim 1, it is characterised in that
Division arithmetic is in the step (2)The detailed information of face is represented due to R (x, y), is fast change, and L (x, y) generation
Table illumination component, is slow change, is being locally approximate constant, so eliminating L (x, y) points come approximate by division arithmetic
Amount, obtains formula (4)
。
5. the human face light invariant feature extraction method based on Sobel operators according to claim 4, it is characterised in that
Illumination component L (x, y) change is slow, so there is formula (5)：
L0,0≈L-1,-1≈L0,-1≈L1,-1≈L-1,1≈L0,1≈L1,1(5)；
All variables for formula (5) use LtSubstitute, and substitute into formula (4), obtain formula (6)：
In formula (6), illumination component is eliminated.
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Cited By (3)
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| CN107358178A (en) * | 2017-06-27 | 2017-11-17 | 重庆三峡学院 | A kind of human face light invariant feature extraction method based on Roberts operators |
| CN107437061A (en) * | 2017-06-27 | 2017-12-05 | 重庆三峡学院 | It is a kind of to utilize logarithmic transformation and the human face light invariant feature extraction method of Roberts operators |
| CN107451591A (en) * | 2017-06-27 | 2017-12-08 | 重庆三峡学院 | A kind of human face light invariant feature extraction method using Wallis operators |
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Application publication date: 20170613 |