CN110399814A - It is a kind of to indicate that field adapts to the face identification method of measurement based on local linear - Google Patents
It is a kind of to indicate that field adapts to the face identification method of measurement based on local linear Download PDFInfo
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Abstract
A kind of to indicate that field adapts to the face identification method of measurement based on local linear, technical solution is: vector data point X pretreated for a width facial imageiSelection and the pretreated vector data point X of a width facial image in matrix data X are constituted from the pretreated vector data point of all width facial images respectivelyiThe identical K Neighbor Points of classification carry out local linear expression, obtaining local linear indicates coefficient, establish the Partial Reconstruction error matrix indicated based on local linear, then the divergence between different classes of multiple manifold is measured with logarithm Euclidean distance, simultaneously, also using the diversity between logarithm Euclidean distance measurement training data domain and test data domain, finally by the divergence maximized between multiple manifold, minimize the difference between source data and target data, it finds low-dimensional and differentiates subspace, realize that facial image differentiates feature extraction.The present invention extracts the characteristic of division of facial image by maximizing the similitude that multiple manifold divergence and training, test data are distributed, and has the characteristics that improve facial image recognition effect.
Description
Technical field
The invention belongs to technical field of face recognition, indicate that field adapts to measurement based on local linear more particularly to one kind
Face identification method.
Background technique
Recognition of face is one of machine vision biological identification major product form, is the indispensabilities such as public safety, daily life
Technology.In recent years, face recognition application range is continuously increased, and requires high accuracy.And in its equipment imaging process,
Illumination, block and posture label etc. due to, cause face recognition algorithms that can not effectively extract feature, these defective effects
Algorithm discrimination reduces the effect of face recognition technology in actual use, or even brings security risk.Therefore recognition of face is calculated
Method stability is particularly important.
Face recognition algorithms experienced multiple developing stage.The characterization for such as obtaining low dimensional, the face based on local feature
Identification, the partial descriptions submethod based on study.There is the deep learning method based on deep neural network again at present, this method is known
Other ability is strong, robust is good, but structure is complicated, and calculation amount is very big.How to obtain that a kind of recognition capability is strong, robust is good, with constantly
Between space complexity it is smaller, for the method that a small amount of training set sample still maintains preferable recognition effect, become recognition of face
Hot topic.
Summary of the invention
The present invention is directed to overcome the shortage of prior art, it is therefore an objective to provide it is a kind of can improve model recognition capability and robustness,
A kind of local linear for reducing time space complexity indicates that field adapts to the face identification method of measurement.
To realize that above-mentioned technical proposal, the technical solution adopted by the present invention comprise the concrete steps that:
S1, the facial image of acquisition is pre-processed, obtains pretreated facial image vector data matrix X=
[X1, X2..., XC], in which: C is classification number,M is the characteristic dimension after vectorization, NcIt is c-th of class
Image category quantity, 1≤c≤C；
S2, for c-th of class Xc, from XcThe middle pretreated vector data point x of selection piece imagei, choose and a width figure
As pretreated vector data point xiK nearest vector data point x of Euclidean distancei1, xi2..., xiK, pre- to piece image
Treated vector data point xiLocal linear expression is carried out, linear expression mean square error function is minimized, obtains minimal linear
Indicate coefficient vector Wc, by weight coefficient vector WcN-dimensional is extended to, wherein minimal linear between the vector point with neighbor relationships
Indicate that coefficient is constant, other are 0；
S3, to the data point of remaining class in image vector data matrix X, repeat the above steps, obtain the power of all categories
Weight coefficient matrix W=[W1, W2..., WC]∈RN×N, wherein N is the total number of all pretreated images,
S4, it is based on calculated weight coefficient matrix W=[W1, W2..., WC]∈RN×N, the reconstruction error of each class is calculated,
Obtain reconstruction error matrix R=[R1, R2..., RC]；
S5, to pretreated facial image vector data matrix X=[X1, X2..., XC] training dataset XSAnd survey
Try data set XTAny data point xj, step S2, S3, S4 are repeated, training dataset X is respectively obtainedSIn reconstructed error matrix
RS=[RS1, RS2..., RSc] and test data set XTReconstructed error matrix RT=[RT1, RT2..., RTc]；
S6, it is based on the reconstruction error matrix, constructs objective function, obtains low dimension projective matrix A；
S7, the low dimension projective matrix, vector data point x pretreated for piece image are based oni, calculate its process
Low-dimensional characteristic Y i after linear projection；
S8, using nearest neighbor algorithm to the low-dimensional characteristic YiClassify, realizes the identification of face characteristic.
Further, according to claim 1 to indicate that field adapts to the recognition of face side of measurement based on local linear
Method, which is characterized in that carrying out pretreatment to the facial image of acquisition in step S1 includes that the gray processing successively carried out is handled, smoothly
Processing, normalized and vectorization processing.
Further, according to claim 1 to indicate that field adapts to the recognition of face side of measurement based on local linear
Method, which is characterized in that in step S2, the minimal linear indicates mean square error function are as follows:
Wherein: xiIndicate the pretreated vector data point of piece image, i.e. XcI-th column,
xij(j=1,2 ..., K) indicates XcIn with the pretreated vector data point x of piece imageiEuclidean distance is nearest
K vector data point,
WI, jIndicate the weight coefficient of j Neighbor Points of the i-th width image, i.e. WcIn the i-th row each column.
Further, according to claim 1 to indicate that field adapts to the recognition of face side of measurement based on local linear
Method, which is characterized in that in step S4, the reconstruction error matrix of each class is expressed as:
Wherein: the transposition operation of T representing matrix；
I is and WcThe identical unit matrix of dimension.
Further, according to claim 1 to indicate that field adapts to the recognition of face side of measurement based on local linear
Method, which is characterized in that step S6 is specifically included:
One S61, application small disturbance make RcAs positive definite matrix:
Wherein: IcBe withThe identical unit matrix of quasi- number；
δ indicates disturbance size；
S62, log-Euclidean distance metric difference symmetric positive definite matrix R is usedcThe distance between, establish multiple manifold
Between divergence SM:
S63, log-Euclidean distance metric training dataset X is usedSWith test data set XTDistinctiveness ratio Sd:
S64, in lower dimensional space, establish objective function:
s.t.ATA=I
Wherein, A ∈ Rd×NIndicate low dimension projective matrix,
D is the Spatial Dimension after dimensionality reduction,
Log () is indicated using e as the logm operation at bottom；
I indicates unit matrix.
Further, according to claim 1 to indicate that field adapts to the recognition of face side of measurement based on local linear
Method, which is characterized in that the low-dimensional characteristic Y in step S6, after linear projectioniIt indicates are as follows:
Yi=ATXi (7)
Due to the adoption of the above technical scheme, the beneficial effects of the present invention are:
The present invention indicates that field adapts to the face identification method of measurement using local linear, on the one hand for a width face figure
As pretreated vector data point Xi, matrix data is constituted from the pretreated vector data point of all width facial images respectively
In X, selection and the pretreated vector data point X of a width facial imagei, it is local that the identical K Neighbor Points of classification establish manifold
Linear expression, then with log-Euclidean distance metric manifold local linear indicate between multiple manifold divergence, keep flow
On the basis of shape partial structurtes are constant, multiple manifold divergence is maximized to find low dimension projective matrix A, on the other hand, in order to guarantee
Similitude in space in the projected between source data domain and target data domain minimizes the covariance distance of the two, with this
The differentiation feature extraction for realizing facial image, improves the effect of recognition of face.
Therefore, the present invention has identification energy by maximizing the similarity feature between source data domain and target data domain
Power is strong, robust is good, while time space complexity is smaller, and the spy of preferable recognition effect is still maintained for a small amount of training set sample
Point.
Specific embodiment
The invention will be further described With reference to embodiment, not to the limitation of its protection scope.
Embodiment 1
It is a kind of to indicate that field adapts to the face identification method of measurement based on local linear.The present embodiment the method it is specific
Step is:
Step 1: at the gray processing processing, smoothing processing, normalized and the vectorization that are carried out to the facial image of acquisition
Reason, obtains pretreated facial image vector data matrix X=[X1, X2..., XC], in which: C is classification number,M is the characteristic dimension after vectorization, NcIt is the image category quantity of c-th of class, 1≤c≤C；
Step 2: for c-th of class Xc, from XcThe middle pretreated vector data point x of selection piece imagei, choose and one
Vector data point x after width image preprocessingiK nearest vector data point x of Euclidean distancei1, xi2..., xiK, to a width figure
As pretreated vector data point xiLocal linear expression is carried out, linear expression mean square error function, minimal linear are minimized
Indicate mean square error function are as follows:
Wherein: xiIndicate the pretreated vector data point of piece image, i.e. XcI-th column,
xij(j=1,2 ..., K indicate XcIn with the pretreated vector data point x of piece imageiEuclidean distance is nearest
K vector data point,
WI, jIndicate the weight coefficient of j Neighbor Points of the i-th width image, i.e. WcIn the i-th row each column.
Minimal linear, which is calculated, indicates coefficient vector Wc, by weight coefficient vector WcN-dimensional is extended to, wherein having neighbour
Minimal linear indicates that coefficient is constant between the vector point of relationship, other are 0；
Step 3: repeating the above steps to the data point of remaining class in image vector data matrix X, obtaining all categories
Weight coefficient matrix W=[W1, W2..., WC]∈RN×N, wherein N is the total number of all pretreated images,
Step 4: being based on calculated weight coefficient matrix W=[W1, W2..., WC]∈RN×N, the reconstruction error of each class
Matrix is expressed as:
Wherein: the transposition operation of T representing matrix；
I is and WcThe identical unit matrix of dimension.
The reconstruction error for calculating each class obtains reconstruction error matrix R=[R1, R2..., RC]；
Step 5: to pretreated facial image vector data matrix, X=[X1, X2..., XC] training dataset XS
With test data set XTAny data point xj, step S2, S3, S4 are repeated, training dataset X is respectively obtainedSIn reconstructed error
Matrix Rs=[RS1, RS2..., RSC] and test data set XTReconstructed error matrix RT=[RT1, RT2..., RTC]；
Step 6: applying a small disturbance makes RcAs positive definite matrix:
Wherein: IcBe withThe identical unit matrix of dimension；
δ indicates disturbance size；
Then log-Euclidean distance metric difference symmetric positive definite matrix R is usedcThe distance between, establish multiple manifold
Between divergence SM:
Use log-Euclidean distance metric training dataset XSWith test data set XTDistinctiveness ratio Sd:
In lower dimensional space, objective function is established:
Wherein, A ∈ Rd×NIndicate low dimension projective matrix,
D is the Spatial Dimension after dimensionality reduction,
Log () is indicated using e as the logm operation at bottom；
I indicates unit matrix.
It maximizes objective function and obtains matrix A.
Step 7: being based on the low dimension projective matrix, vector data point x pretreated for piece imagei, by line
Property projection after low-dimensional characteristic YiIt indicates are as follows:
Yi=ATXi (7)
Calculate its low-dimensional characteristic Y after linear projectioni；
Step 8: using nearest neighbor algorithm to the low-dimensional characteristic YiClassify, realizes the identification of face characteristic.
The beneficial effect of present embodiment is:
The present invention, which is used, indicates that field adapts to the face identification method of measurement based on local linear, on the one hand for a width people
The pretreated vector data point X of face imagei, matrix is constituted from the pretreated vector data point of all width facial images respectively
In data X, selection and the pretreated vector data point X of a width facial imagei, the identical K Neighbor Points of classification establish manifold
Local linear indicate, then with log-Euclidean distance metric manifold local linear indicate between multiple manifold divergence, protecting
Hold manifold partial structurtes it is constant on the basis of, maximize multiple manifold divergence and find low dimension projective matrix A, on the other hand, in order to
Guarantee the similitude in space in the projected between source data domain and target data domain, minimize the covariance distance of the two,
The differentiation feature extraction that facial image is realized with this, improves the effect of recognition of face.
Therefore, the present invention has identification energy by maximizing the similarity feature between source data domain and target data domain
Power is strong, robust is good, while time space complexity is smaller, and the spy of preferable recognition effect is still maintained for a small amount of training set sample
Point.
Claims (6)
1. a kind of indicate that field adapts to the face identification method of measurement based on local linear, which comprises the steps of:
S1, the facial image of acquisition is pre-processed, obtains pretreated facial image vector data matrix X=[X1,
X2..., XC], in which: C is classification number,M is the characteristic dimension after vectorization, NcIt is the image of c-th of class
Categorical measure, 1≤c≤C；
S2, for c-th of class Xc, from XcThe middle pretreated vector data point x of selection piece imagei, choose pre- with piece image
Treated vector data point xiK nearest vector data point x of Euclidean distancei1, xi2..., xiK, piece image is pre-processed
Vector data point x afterwardsiLocal linear expression is carried out, linear expression mean square error function is minimized, obtains minimal linear expression
Coefficient vector Wc, by weight coefficient vector WcN-dimensional is extended to, wherein minimal linear indicates between the vector point with neighbor relationships
Coefficient is constant, other are 0；
S3, to the data point of remaining class in image vector data matrix X, repeat the above steps, obtain the weight system of all categories
Matrix number W=[W1, W2..., WC]∈RN×N, wherein N is the total number of all pretreated images,
S4, it is based on calculated weight coefficient matrix W=[W1, W2..., WC]∈RN×N, the reconstruction error of each class is calculated, is obtained
Reconstruction error matrix R=[R1, R2..., RC]；
S5, to pretreated facial image vector data matrix X=[X1, X2..., XC] training dataset XSAnd test data
Collect XTAny data point xj, step S2, S3, S4 are repeated, training dataset X is respectively obtainedSIn reconstructed error matrix Rs=
[RS1, RS2..., RSC] and test data set XTReconstructed error matrix RT=[RT1, RT2..., RTC]；
S6, it is based on the reconstruction error matrix, constructs objective function, obtains low dimension projective matrix A；
S7, the low dimension projective matrix, vector data point x pretreated for piece image are based oni, it is calculated by linear
Low-dimensional characteristic Y after projectioni；
S8, using nearest neighbor algorithm to the low-dimensional characteristic YiClassify, realizes the identification of face characteristic.
2. according to claim 1 indicate that field adapts to the face identification method of measurement based on local linear, feature exists
In carrying out pretreatment to the facial image of acquisition in step S1 includes the gray processing processing successively carried out, smoothing processing, normalization
Processing and vectorization processing.
3. according to claim 1 indicate that field adapts to the face identification method of measurement based on local linear, feature exists
In in step S2, the minimal linear indicates mean square error function are as follows:
Wherein: xiIndicate the pretreated vector data point of piece image, i.e. XcI-th column,
xij(j=1,2 ..., K) indicates XcIn with the pretreated vector data point x of piece imageiEuclidean distance nearest K
Vector data point,
WI, jIndicate the weight coefficient of j Neighbor Points of the i-th width image, i.e. WcIn the i-th row each column.
4. according to claim 1 indicate that field adapts to the face identification method of measurement based on local linear, feature exists
In in step S4, the reconstruction error matrix of each class is expressed as:
Wherein: the transposition operation of T representing matrix；
I is and WcThe identical unit matrix of dimension.
5. according to claim 1 indicate that field adapts to the face identification method of measurement based on local linear, feature exists
In step S6 is specifically included:
One S61, application small disturbance make RcAs positive definite matrix:
Wherein: IcBe withThe identical unit matrix of dimension；
δ indicates disturbance size；
S62, log-Euclidean distance metric difference symmetric positive definite matrix R is usedcThe distance between, it establishes between multiple manifold
Divergence SM:
S63, log-Euclidean distance metric training dataset X is usedSWith test data set XTDistinctiveness ratio Sd:
S64, in lower dimensional space, establish objective function:
s.t.ATA=I
Wherein, A ∈ Rd×NIndicate low dimension projective matrix,
D is the Spatial Dimension after dimensionality reduction,
Log () is indicated using e as the logm operation at bottom；
I indicates unit matrix.
6. according to claim 1 indicate that field adapts to the face identification method of measurement based on local linear, feature exists
In low-dimensional characteristic Y in step S6, after linear projectioniIt indicates are as follows:
Yi=ATXi。 (7)
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110956098A (en) * | 2019-11-13 | 2020-04-03 | 深圳和而泰家居在线网络科技有限公司 | Image processing method and related equipment |
| CN111144240A (en) * | 2019-12-12 | 2020-05-12 | 深圳数联天下智能科技有限公司 | Image processing method and related equipment |
| CN115019368A (en) * | 2022-06-09 | 2022-09-06 | 南京审计大学 | Face recognition feature extraction method in audit investigation based on 2DESDLPP |
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| CN103336960A (en) * | 2013-07-26 | 2013-10-02 | 电子科技大学 | Human face identification method based on manifold learning |
| CN107463920A (en) * | 2017-08-21 | 2017-12-12 | 吉林大学 | A kind of face identification method for eliminating partial occlusion thing and influenceing |
| WO2017219391A1 (en) * | 2016-06-24 | 2017-12-28 | 深圳市唯特视科技有限公司 | Face recognition system based on three-dimensional data |
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| CN103336960A (en) * | 2013-07-26 | 2013-10-02 | 电子科技大学 | Human face identification method based on manifold learning |
| WO2017219391A1 (en) * | 2016-06-24 | 2017-12-28 | 深圳市唯特视科技有限公司 | Face recognition system based on three-dimensional data |
| CN107463920A (en) * | 2017-08-21 | 2017-12-12 | 吉林大学 | A kind of face identification method for eliminating partial occlusion thing and influenceing |
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| CN110956098A (en) * | 2019-11-13 | 2020-04-03 | 深圳和而泰家居在线网络科技有限公司 | Image processing method and related equipment |
| CN111144240A (en) * | 2019-12-12 | 2020-05-12 | 深圳数联天下智能科技有限公司 | Image processing method and related equipment |
| CN115019368A (en) * | 2022-06-09 | 2022-09-06 | 南京审计大学 | Face recognition feature extraction method in audit investigation based on 2DESDLPP |
| CN115019368B (en) * | 2022-06-09 | 2023-09-12 | 南京审计大学 | Face recognition feature extraction method in audit investigation |
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