CN106022223B - A kind of higher-dimension local binary patterns face identification method and system - Google Patents
A kind of higher-dimension local binary patterns face identification method and system Download PDFInfo
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Abstract
The invention discloses a kind of higher-dimension local binary patterns face recognition algorithms and system, which and pre-processes it to obtain the gray level image of identical size the following steps are included: S1, obtain facial image；S2, HDLBP feature extraction is carried out to pretreated gray level image, obtains corresponding characteristic image；S3, the histogram for extracting characteristic image, obtain corresponding feature vector；S4, it is compared according to feature vector with the information in property data base, obtains recognition result.The present invention can extract the local feature and global characteristics of image, and the discrimination of algorithm greatly improved；And on the basis of guaranteeing that algorithm complexity is not high, the accuracy rate of image recognition is increased.
Description
Technical field
The present invention relates to field of face identification more particularly to a kind of higher-dimension local binary patterns face identification method and it is
System.
Background technique
Local binary patterns (Local Binary pattern, the LBP) one kind of algorithm as face recognition algorithms, be by
Ojala, Ahonen are equal to a kind of algorithm dependent on local grain description proposed for 1996, for describing pixel and its neighbour
The relationship of pixel numerically in domain, because its calculation method simplicity, to the local feature of image have well it is descriptive, to light
According to insensitivity the features such as and widely used in field of face identification.Simultaneously as LBP description has only focused on figure
The description of the local feature of picture has ignored the description to image overall feature, results in LBP algorithm in global characteristics extraction
It is insufficient.For effective solution this problem, numerous scholars study it, and propose many improvement and optimization
Method.
The method that all nighttides etc. propose piecemeal processing is asked to which LBP to be solved description is insufficient on extracting global characteristics
Topic.Dividing the core concept handled fastly is divided according to certain size etc. point original image, or according to the position where the face of people
Original image extracts LBP feature to subgraph respectively, and all feature vectors are concatenated together, and obtains some characteristics in the overall situation, leads to
Cross experiments have shown that after point fast processing the performance of LBP algorithm be better than it is untreated before LBP algorithm, but to original image according to one
Which type of standard divide on this point the but answer of none affirmative.It can neither be obtained very in the processing to original image
Good effect, then Wang Hong etc. is studied in itself from LBP description, and thinking is amplification LBP description by equimultiple, is made
It can extract characteristics of image in larger scope.What is be compared is no longer some pixel, but including some picture
The mean value of the pixel in fixation neighborhood including vegetarian refreshments is calculated, and ensure that local characteristics also embody to a certain extent
Global property, but become the emphasis of research again for the selection of Size of Neighborhood.It is equally to describe sub- from BLP,
Different from the principle that equimultiple is amplified, king is global special at equal feature instantiation passed through in the multi-level neighborhood of multiple dimensioned weighted center point
It levies, the point distance center point in neighborhood is closer, and weight when being weighted is heavier, and the level of weighting is more, the overall situation of embodiment
Property is better, while computation complexity is higher.
Summary of the invention
The technical problem to be solved in the present invention is that providing one for the defect that cannot extract global characteristics in the prior art
Kind can greatly improve the higher-dimension local binary patterns face identification method and system of algorithm discrimination.
The technical solution adopted by the present invention to solve the technical problems is:
The present invention provides a kind of higher-dimension local binary patterns face recognition algorithms, comprising the following steps:
S1, facial image is obtained, and it is pre-processed to obtain the gray level image of identical size；
S2, HDLBP feature extraction is carried out to pretreated gray level image, obtains corresponding characteristic image；
S3, the histogram for extracting characteristic image, obtain corresponding feature vector；
S4, it is compared according to feature vector with the information in property data base, obtains recognition result.
Further, the method for obtaining gray level image is pre-processed in step S1 of the invention specifically:
If the distribution of the local grain V of face gray level image are as follows:
V=v (gc g0 … gp-1 g)
Wherein, gcRepresent the central threshold of window, gkMiddle k=0,2...p-1, gkIndicate the gray value of each neighborhood territory pixel point,
P indicates neighborhood point number, and g indicates the gray average of face gray level image, calculation formula are as follows:
Wherein, m × n is the size of gray level image, and g (i j) is the gray value of each pixel in image.
Further, the method for HDLBP feature extraction is carried out in step S2 of the invention specifically:
HDLBP description has adopted calculation method of classical LBP description in window first, has guaranteed when calculating
Local feature；Then same calculating side is used to the gray average of window center pixel gray value and face gray level image
Method guarantees global characteristics；Finally using local feature as low-dimensional, global characteristics are combined as higher-dimension, the result of calculating
It is exactly the characteristic value of central pixel point in the window.
Further, the method that higher-dimension and low-dimensional fusion are carried out in step S2 of the invention specifically:
Be added to central feature as the component of most higher-dimension in the binary sequence of edge feature so that characteristic sequence to
High one-dimensional stretching, extension expands the information content that characteristic sequence includes；According to the following formula by low-dimensional feature and high dimensional feature fusion one
It rises, so that two column characteristic sequences is become a column characteristic sequence, and calculate according to the metric method of binary system turn and can be obtained
Corresponding characteristic value；Calculation formula are as follows:
Wherein, gcRepresent the central threshold of window, gkMiddle k=0,2...p-1, gkIndicate the gray value of each neighborhood territory pixel point,
P indicates neighborhood point number, and g indicates the gray average of face gray level image, calculation formula are as follows:
Wherein, m × n is the size of gray level image, and g (i j) is the gray value of each pixel in image.
Further, the formula of HDLBP feature extraction is carried out in step S2 of the invention are as follows:
Wherein, s function is as follows:
Further, the method for the histogram of characteristic image is extracted in step S3 of the invention specifically:
According to the characteristic image of input, pixel all in image is subjected to ascending order row according to the size of its gray value
Sequence, the number that then there is statistics the pixel of same grayscale value to occur, obtains a n*1 sequence, i.e. feature vector, wherein n
Indicate that the number of different gray values in characteristic image, the calculation formula of histogram are as follows:
H (i)=NUM (gi)i∈(1,n)
Wherein h (i) indicates that gray value is giPixel number.
Further, the method for recognition result is obtained in step S4 of the invention specifically:
It is compared according to feature vector with the information in property data base, is to measure with Euclidean distance, utilization is closest
Classification is identified.
The present invention provides a kind of higher-dimension local binary patterns face identification system, comprising:
Image pre-processing unit for obtaining facial image, and pre-processes it to obtain the grayscale image of identical size
Picture；
HDLBP feature extraction unit is corresponded to for carrying out HDLBP feature extraction to pretreated gray level image
Characteristic image；
Characteristic vector pickup unit obtains corresponding feature vector for extracting the histogram of characteristic image；
Image identification unit obtains identification knot for being compared according to feature vector with the information in property data base
Fruit.
The beneficial effect comprise that: higher-dimension local binary patterns face recognition algorithms of the invention, by adopting
Calculation method of classical LBP description in window, ensure that local feature；Then to window center pixel gray value and
The gray average of face gray level image uses same calculation method, guarantees global characteristics；Finally using local feature as low-dimensional,
Global characteristics are combined as higher-dimension, the characteristic value as central pixel point in the window；Algorithm of the invention can mention
The local feature and global characteristics for taking image, greatly improved the discrimination of algorithm；And guaranteeing the not high base of algorithm complexity
On plinth, the accuracy rate of image recognition is increased.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is the flow chart of the higher-dimension local binary patterns face recognition algorithms of the embodiment of the present invention；
Fig. 2 is the fusion process of the higher-dimension local binary patterns face recognition algorithms of the embodiment of the present invention；
Fig. 3 is the block diagram of the higher-dimension local binary patterns face recognition algorithms of the embodiment of the present invention；
Fig. 4 is the calculating process figure of the higher-dimension local binary patterns face recognition algorithms of the embodiment of the present invention；
Fig. 5 is the block diagram of the higher-dimension local binary patterns face identification system of the embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
As shown in Figure 1, the higher-dimension local binary patterns face recognition algorithms of the embodiment of the present invention, comprising the following steps:
S1, facial image is obtained, and it is pre-processed to obtain the gray level image of identical size；Pretreatment obtains gray scale
The method of image specifically:
If the distribution of the local grain V of face gray level image are as follows:
V=v (gc g0 … gp-1 g)
Wherein, gcRepresent the central threshold of window, gkMiddle k=0,2...p-1, gkIndicate the gray value of each neighborhood territory pixel point,
P indicates neighborhood point number, and g indicates the gray average of face gray level image, calculation formula are as follows:
Wherein, m × n is the size of gray level image, and g (i j) is the gray value of each pixel in image.
S2, HDLBP feature extraction is carried out to pretreated gray level image, obtains corresponding characteristic image；Carry out HDLBP
The formula of feature extraction are as follows:
Wherein, s function is as follows:
S3, the histogram for extracting characteristic image, obtain corresponding feature vector；
S4, it is compared according to feature vector with the information in property data base, obtains recognition result.
HDLBP description has adopted calculation method of classical LBP description in window first, has guaranteed when calculating
Local feature；Then same calculating side is used to the gray average of window center pixel gray value and face gray level image
Method guarantees global characteristics；Finally using local feature as low-dimensional, global characteristics are combined as higher-dimension, the result of calculating
It is exactly the characteristic value of central pixel point in the window.
As shown in Fig. 2, the process of fusion specifically:
Central feature is added in the binary sequence of edge feature as the component of most higher-dimension, so that characteristic sequence is to height
One-dimensional stretching, extension, the information content that characteristic sequence includes are bigger.Since low-dimensional feature and high dimensional feature are fused together, so that two column are special
When levying sequence and become a column characteristic sequence, therefore characteristic sequence being converted to characteristic value, it is only necessary to turn the decimal system according to binary system
Method carry out calculate corresponding characteristic value, calculation formula can be obtained are as follows:
As shown in figure 3, in another specific embodiment of the invention,
The specific calculating process of algorithm is as follows:
Step 1: inputting facial image to be identified；
Step 2: the face picture to input pre-processes, the gray level image of identical size is obtained；
Step 3: extracting the HDLBP feature of facial image to be identified according to the following formula, corresponding characteristic image is obtained；
According to the gray level image of input, the formula that g is calculated as follows calculates the value of g, then utilizes HDLBP feature
Calculation formula to gray level image from left to right, be scanned from top to bottom, for i each time, the value of j has a correspondence
Characteristic value, by eigenvalue cluster at image be characteristic image.
Step 4: extracting the histogram of characteristic image, corresponding feature vector is obtained；
According to the characteristic image of input, pixel all in image is subjected to ascending order row according to the size of its gray value
Sequence, the number that then there is statistics the pixel of same grayscale value to occur, obtains a n*1 sequence, i.e. feature vector, wherein n
Indicate that the number of different gray values in characteristic image, the calculation formula of histogram are as follows:
H (i)=NUM (gi)i∈(1,n)
Wherein h (i) indicates that gray value is giPixel number.
Step 5: being to measure with Euclidean distance, being known using nearest neighbour classification on the property data base of foundation
Not；
Euclidean distance calculation formula in n-dimensional space is as follows:
Wherein x1kAnd x2kIt is n-dimensional vector x respectively1And x2Kth be component.Nearest neighbor classification: data to be sorted are pressed
Range from be divided into in the class where sample nearest with a distance from it.
The Euclidean distance of all samples in feature vector and property data base is calculated using the formula of Euclidean distance, wherein
Sample with minimum range is the result of identification.Wherein, dmIt is feature vector xkWith m-th of sample y in databasekEurope
Family name's distance.
Step 6: the result of output identification.
As shown in figure 4, being tested using ORL face database and YALE face database, ORL face database is by Cambridge University's media
AT&T establishment of laboratory.The library includes 40 class faces, every 10 width of class, the gray level image that size is 112 × 92.All images
There is similar dark background, the different images of same people are in different time, different illumination, different head posture, different expressions
Made of being shot under different details；YALE face database is the face database that Yale establishes, which includes
15 people, everyone 11 width, including different expressions, different direction of illuminations and variations in detail etc..Face database is uniformly divided into test
Sample and experiment sample, for ORL face database, test sample: experiment sample=5:5；To YALE face database, test sample: real
Test sample=6:5.
The experimental results are shown inthe following table.
Therefore: whether in YALE face database or on ORL face database, the knowledge of higher-dimension local binary patterns algorithm
Rate will not be higher than traditional local binary patterns.
The shortcomings that global characteristics cannot be extracted for the face recognition algorithms of traditional local binary patterns, propose a kind of base
In the face recognition algorithms of higher-dimension local binary patterns.This algorithm is greatly mentioned relative to traditional local binary patterns algorithm
The high discrimination of algorithm.
As shown in figure 5, the higher-dimension local binary patterns face identification system of the embodiment of the present invention, for realizing of the invention real
Apply the higher-dimension local binary patterns face recognition algorithms of example, comprising:
Image pre-processing unit for obtaining facial image, and pre-processes it to obtain the grayscale image of identical size
Picture；
HDLBP feature extraction unit is corresponded to for carrying out HDLBP feature extraction to pretreated gray level image
Characteristic image；
Characteristic vector pickup unit obtains corresponding feature vector for extracting the histogram of characteristic image；
Image identification unit obtains identification knot for being compared according to feature vector with the information in property data base
Fruit.
It should be understood that for those of ordinary skills, it can be modified or changed according to the above description,
And all these modifications and variations should all belong to the protection domain of appended claims of the present invention.
Claims (3)
1. a kind of higher-dimension local binary patterns face identification method, which comprises the following steps:
S1, facial image is obtained, and it is pre-processed to obtain the gray level image of identical size；
S2, HDLBP feature extraction is carried out to pretreated gray level image, obtains corresponding characteristic image；
S3, the histogram for extracting characteristic image, obtain corresponding feature vector；
S4, it is compared according to feature vector with the information in property data base, obtains recognition result；
The method of HDLBP feature extraction is carried out in step S2 specifically:
HDLBP description has adopted calculation method of LBP description in window first, ensure that local feature when calculating；
Then calculating of the son in window is described with LBP to the gray average of window center pixel gray value and face gray level image
Method guarantees global characteristics；Finally using local feature as low-dimensional, global characteristics are combined as higher-dimension, the knot of calculating
Fruit is exactly the characteristic value of central pixel point in the window；
The method that higher-dimension and low-dimensional fusion are carried out in step S2 specifically:
It is added to central feature as the component of most higher-dimension in the binary sequence of edge feature, so that characteristic sequence Xiang Gaoyi
Dimension stretching, extension expands the information content that characteristic sequence includes；Low-dimensional feature and high dimensional feature are fused together according to the following formula, made
Two column characteristic sequences become a column characteristic sequence, and according to binary system turn metric method calculate can be obtained it is corresponding
Characteristic value；Calculation formula are as follows:
Wherein, gcRepresent the central threshold of window, gkMiddle k=0,2...p-1, gkIndicate the gray value of each neighborhood territory pixel point, p table
Show neighborhood point number, g indicates the gray average of face gray level image, calculation formula are as follows:
Wherein, m × n is the size of gray level image, and g (i j) is the gray value of each pixel in image；
The formula of HDLBP feature extraction is carried out in step S2 are as follows:
Wherein, s function is as follows:
2. higher-dimension local binary patterns face identification method according to claim 1, which is characterized in that extracted in step S3
The method of the histogram of characteristic image specifically:
According to the characteristic image of input, pixel all in image is subjected to ascending sort according to the size of its gray value, so
The number that there is statistics the pixel of same grayscale value to occur afterwards, obtains a n*1 sequence, i.e. feature vector, and wherein n indicates special
The number of different gray values in image is levied, the calculation formula of histogram is as follows:
H (i)=NUM (gi)i∈(1,n)
Wherein h (i) indicates that gray value is giPixel number.
3. higher-dimension local binary patterns face identification method according to claim 1, which is characterized in that obtained in step S4
The method of recognition result specifically:
It is compared according to feature vector with the information in property data base, is to measure with Euclidean distance, utilizes nearest neighbour classification
Method is identified.
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| CN110309692B (en) * | 2018-03-27 | 2023-06-02 | 杭州海康威视数字技术股份有限公司 | Face recognition method, device and system, and model training method and device |
| CN110543853A (en) * | 2019-09-04 | 2019-12-06 | 上海观安信息技术股份有限公司 | Method for realizing face recognition processing based on image processing technology |
| CN110956090B (en) * | 2019-11-04 | 2022-08-30 | 南京邮电大学 | Face feature extraction method based on fusion of positive and negative illumination invariant units |
| CN111709312B (en) * | 2020-05-26 | 2023-09-22 | 上海海事大学 | Local feature face recognition method based on combined main mode |
| CN111832639B (en) * | 2020-06-30 | 2022-05-31 | 山西大学 | Drawing emotion prediction method based on transfer learning |
| CN116719966B (en) * | 2023-05-25 | 2024-03-08 | 中国医学科学院皮肤病医院(中国医学科学院皮肤病研究所) | Hospital patient information management system |
| CN117373100B (en) * | 2023-12-08 | 2024-02-23 | 成都乐超人科技有限公司 | Face recognition method and system based on differential quantization local binary pattern |
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