CN111241960B - Face recognition method and system based on wiener filtering and PCA - Google Patents
Face recognition method and system based on wiener filtering and PCA Download PDFInfo
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Abstract
The invention relates to a face recognition method and a face recognition system based on wiener filtering and PCA, wherein the face recognition method and the face recognition system comprise the following steps: step 201, acquiring image information of a user as a first image; step 202, preprocessing the first image to obtain a second image; step 203, performing face detection on the second image, and reducing the region of interest of the second image to obtain a third image; and 204, extracting features of the third image in a PCA mode, and completing face recognition through an RBF-SVM classifier according to the extracted features. According to the face recognition method, the face image can be denoised in a wiener filtering mode, the dimension reduction is carried out on the denoised face image through PCA to extract effective features, and finally the face recognition is carried out in an RBF-SVM mode, so that the face recognition speed and the robustness can be effectively improved.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a face recognition method and system based on wiener filtering and PCA.
Background
The face recognition technology is a biological recognition technology, is applied to automatically recognizing people based on face features, and integrates various technologies such as digital images, video processing, mode recognition and the like. Since the 90 s of the 20 th century, face recognition has become a research hotspot in the fields of pattern recognition and artificial intelligence. Along with the rapid development of the face recognition technology, a statistical recognition method based on learning becomes a mainstream method of face recognition, and time complexity and space complexity are increased in the calculation process due to the high feature dimension. It is difficult to estimate parameters of the high-dimensional feature space, so that efficient face recognition features obtained on the original high-dimensional feature image by feature extraction or feature transformation are necessary.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a face recognition method and a face recognition system based on wiener filtering and PCA, which can denoise face images in a wiener filtering mode, and can reduce dimensions and extract effective characteristics of the denoised face images through PCA, and finally, the face recognition is carried out in an RBF-SVM mode, so that the face recognition speed and the robustness can be effectively improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a face recognition system based on wiener filtering and PCA is provided, which comprises:
the image acquisition unit is used for acquiring image information of a user to obtain a first image;
the image preprocessing unit is used for preprocessing the first image to obtain a second image;
the face region acquisition unit is used for carrying out face detection on the second image and reducing the region of interest of the second image to obtain a third image;
and the face recognition unit is used for extracting the characteristics of the third image in a PCA mode and completing face recognition through an RBF-SVM classifier according to the extracted characteristics.
The invention also provides a face recognition method based on wiener filtering and PCA, which comprises the following steps:
step 201, acquiring image information of a user as a first image;
step 202, preprocessing the first image to obtain a second image;
step 203, performing face detection on the second image, and reducing the region of interest of the second image to obtain a third image;
and 204, extracting features of the third image in a PCA mode, and completing face recognition through an RBF-SVM classifier according to the extracted features.
Further, the operation of preprocessing the first image to obtain the second image in step 202 is specifically that filtering the first image by calling a wiener2 function in MATLAB to obtain the second image, where the wiener2 function specifically includes the following steps:
h=wiener 2 (J, [ m n ], noise), [ H, noise ] =wiener 2 (J, [ m n ]), h=wiener 2 (J, [ m n ], noise), where m and n are both 3 as default values, noise is noise in the image, J is the first image, and H is the second image.
Further, the shrinking the region of interest of the second image to obtain the third image in step 203 specifically includes the following steps:
step 401, acquiring a first address of a second image, wherein the second image is high and wide;
step 402, opening up a memory buffer area, and initializing the memory buffer area to 255;
step 403, performing binarization processing on the second image, and obtaining a face region through OpenCV;
step 404, tracking boundary points of a face area in the face area, finding a boundary point with a binarization value of 1, and setting R, G, B of the point in a memory buffer area to 255;
step 405, repeating step 404 until the tracking initiation point is returned;
and step 406, copying the content of the memory buffer area into the second image to obtain a third image.
Further, the covariance matrix G of the PCA method used in the step 204 is specifically expressed by the following formula,
training the sample X i The matrix a of (a) is shown in the following formula,
the feature vector matrix G of the PCA mode after the dimension reduction is shown in the following formula,
feature vector v of each training sample in PCA mode i Projection space construction u of (2) i As shown in the following formula,
Further, the RBF kernel function K of the RBF-SVM classifier in step 204 is described above RBF The following formula is shown:
the classification hyperplane of the RBF-SVM classifier is obtained as follows:
f(X i )＝sgn{∑h t y t [K RBF (ν i ，ν j )+b]}
wherein h is t And y is t Respectively representing the classification hyperplane, b being a constant.
The beneficial effects of the invention are as follows:
the system and the method can obtain the following beneficial effects:
according to the face recognition method, the face image is denoised in a wiener filtering mode, the dimension reduction is carried out on the denoised face image through PCA, effective features are extracted, and finally the face recognition is carried out in an RBF-SVM mode, so that the face recognition speed and the robustness can be effectively improved.
Drawings
Fig. 1 is a flowchart of a face recognition method based on wiener filtering and PCA according to the present invention.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
Referring to fig. 1, the present invention proposes a face recognition system based on wiener filtering and PCA, comprising:
the image acquisition unit is used for acquiring image information of a user to obtain a first image;
the image preprocessing unit is used for preprocessing the first image to obtain a second image;
the face region acquisition unit is used for carrying out face detection on the second image and reducing the region of interest of the second image to obtain a third image;
and the face recognition unit is used for extracting the characteristics of the third image in a PCA mode and completing face recognition through an RBF-SVM classifier according to the extracted characteristics.
The invention also provides a face recognition method based on wiener filtering and PCA, which comprises the following steps:
step 201, acquiring image information of a user as a first image;
step 202, preprocessing the first image to obtain a second image;
step 203, performing face detection on the second image, and reducing the region of interest of the second image to obtain a third image;
and 204, extracting features of the third image in a PCA mode, and completing face recognition through an RBF-SVM classifier according to the extracted features.
Where PCA is a method that can extract valid features from a high-dimensional feature set, the purpose of which is to find the projection that best represents the raw data in the sense of minimum mean square error. The PCA not only can effectively reduce the size of the face image, but also can retain the main identification information of the face image. In the development process, sirovich and Kirby first propose K-L transformation to achieve optimal representation of face images. Turk and Pentland further propose a concept of "facial features", and later, gao proposes an EFLD face recognition method, which applies different judgment bases to projection of a face feature subspace, so as to achieve improvement of recognition rate.
The essence of signal filtering is to extract effective signals from observed signals, and along with the development of mathematical theory and the demands of practical application, filtering methods based on different principles are continuously proposed, and although the deduction processes are different according to the criteria, the final purpose is to reduce the error of signal estimation, so that the output signal of the filtering system is as close to the practical signal as possible. In order to solve the problem of accurate tracking of a fire control system in the world war of the second time, the wiener sequentially provides an optimal linear filtering theory of a stable random process, and the mathematical statistics knowledge and the linear system theory are connected for the first time, so that the latest estimation theory for smoothing, filtering and predicting random signals is formed. In the development thereafter, wine filtering is applied to more fields and is in use until now.
Support Vector Machines (SVM) were proposed by cornna cores and Vapnik in 1995, the main idea being to build a decision hyperplane that can separate two sample points without errors and maximize the classification gap of the two classes for classification purposes, the point at which this decision plane is determined being called the support vector. Because the distribution of the face image samples is complex, a good classification effect is difficult to obtain by using a simple SVM classification hyperplane. Therefore, the face sample is converted into a high-dimensional space through nonlinear transformation, and a more accurate and more complex hyperplane is constructed, so that a more ideal classification effect is obtained. The functions used in this conversion process are called kernel functions. The RBF kernel function selected herein is a local kernel function that can quickly learn the classification of the actual sample.
PCA is a method by which efficient features can be extracted from a high-dimensional feature set, with the aim of finding the projection that best represents the raw data in the sense of minimum mean square error. The PCA not only can effectively reduce the size of the face image, but also can retain the main identification information of the face image. However, the conventional PCA recognition method can only recognize a face in a controlled environment, and when the face image is affected by different illumination, expression and posture, the recognition rate is greatly reduced. Accordingly, a face recognition method based on wiener filtering and PCA is presented herein, in one aspect, the wiener filtering transform is used to pre-process the original face image. On the other hand, PCA is used to extract the principal component features of the face image. Finally, the features extracted by PCA are learned and classified by RBF-SVM to finish face recognition. The recognition efficiency is superior to that of the method based on PCA and SVM, regardless of the recognition speed and the robustness.
As a preferred embodiment of the present invention, the operation of preprocessing the first image to obtain the second image in the step 202 is specifically that filtering the first image by calling a wiener2 function in MATLAB to obtain the second image, where the wiener2 function specifically includes the following steps:
h=wiener 2 (J, [ m n ], noise), [ H, noise ] =wiener 2 (J, [ m n ]), h=wiener 2 (J, [ m n ], noise), where m and n are both 3 as default values, noise is noise in the image, J is the first image, and H is the second image.
As a preferred embodiment of the present invention, the shrinking the region of interest of the second image to obtain the third image in step 203 specifically includes the following steps:
step 401, acquiring a first address of a second image, wherein the second image is high and wide;
step 402, opening up a memory buffer area, and initializing the memory buffer area to 255;
step 403, performing binarization processing on the second image, and obtaining a face region through OpenCV;
step 404, tracking boundary points of a face area in the face area, finding a boundary point with a binarization value of 1, and setting R, G, B of the point in a memory buffer area to 255;
step 405, repeating step 404 until the tracking initiation point is returned;
and step 406, copying the content of the memory buffer area into the second image to obtain a third image.
In a preferred embodiment of the present invention, the covariance matrix G of the PCA method used in the step 204 is specifically expressed by the following formula,
training the sample X i The matrix a of (a) is shown in the following formula,
the feature vector matrix G after the dimension reduction in the PCA mode is shown in the following formula, and the corresponding feature vector matrix is updated according to the feature values of the covariance matrix ordered according to the descending order. Because the matrix size is too large, singular Value Decomposition (SVD) is used to reduce dimensions,
in the process of obtaining the characteristic value lambda from G i Feature vector v i Thereafter, the feature vector v of each training sample in PCA mode is adopted i Projection space construction u of (2) i As shown in the following formula,
As a preferred embodiment of the present invention, the RBF kernel function K of the RBF-SVM classifier in step 204 is described above RBF The following formula is shown:
the classification hyperplane of the RBF-SVM classifier is obtained as follows:
f(X i )＝sgn{∑h t y t [K RBF (ν i ，ν j )+b]}
wherein h is t And y is t Respectively representing the classification hyperplane, b being a constant.
Because the distribution of the face image samples is complex, a good classification effect is difficult to obtain by using a simple SVM classification hyperplane. Therefore, the face sample is converted into a high-dimensional space through nonlinear transformation, and a more accurate and more complex hyperplane is constructed, so that a more ideal classification effect is obtained. The functions used in this conversion process are called kernel functions. The RBF kernel function selected herein is a local kernel function that can quickly learn the classification of the actual sample.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
While the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiments or any particular embodiment, but is to be construed as providing broad interpretation of such claims by reference to the appended claims in view of the prior art so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
The present invention is not limited to the above embodiments, but is merely preferred embodiments of the present invention, and the present invention should be construed as being limited to the above embodiments as long as the technical effects of the present invention are achieved by the same means. Various modifications and variations are possible in the technical solution and/or in the embodiments within the scope of the invention.
Claims (3)
1. A face recognition system based on wiener filtering and PCA, comprising:
the image acquisition unit is used for acquiring image information of a user to obtain a first image;
the image preprocessing unit is used for preprocessing the first image to obtain a second image;
the face region acquisition unit is used for carrying out face detection on the second image and reducing the region of interest of the second image to obtain a third image;
the face recognition unit is used for extracting the characteristics of the third image in a PCA mode and completing face recognition through an RBF-SVM classifier according to the extracted characteristics;
the operation of preprocessing the first image to obtain a second image is specifically that filtering processing is performed on the first image in a manner of calling a wiener2 function in MATLAB to obtain the second image, wherein the wiener2 function specifically comprises the following steps:
h=wiener 2 (J, [ m n ], noise), [ H, noise ] =wiener 2 (J, [ m n ]), h=wiener 2 (J, [ mn ], noise), where the default values of m and n are both 3, noise is noise in the image, J is the first image, H is the second image;
the covariance matrix G of the PCA mode is specifically expressed as follows,
training the sample X i The matrix a of (a) is shown in the following formula,
the feature vector matrix G of the PCA mode after the dimension reduction is shown in the following formula,
feature vector v of each training sample in PCA mode i Projection space construction u of (2) i As followsAs shown in the drawing,
wherein lambda is i A feature value representing an i-th feature vector;
RBF kernel function K of RBF-SVM classifier RBF The following formula is shown:
the classification hyperplane of the RBF-SVM classifier is obtained as follows:
wherein h is t And y is t Respectively representing the classification hyperplane, b being a constant.
2. The face recognition method based on wiener filtering and PCA is characterized by comprising the following steps of:
step 201, acquiring image information of a user as a first image;
step 202, preprocessing the first image to obtain a second image;
step 203, performing face detection on the second image, and reducing the region of interest of the second image to obtain a third image;
step 204, extracting features of the third image in a PCA mode, and completing face recognition through an RBF-SVM classifier according to the extracted features;
the operation of preprocessing the first image to obtain the second image in step 202 is specifically that filtering the first image to obtain the second image by calling a wiener2 function in MATLAB, where the wiener2 function specifically includes the following steps:
h=wiener 2 (J, [ m n ], noise), [ H, noise ] =wiener 2 (J, [ m n ]), h=wiener 2 (J, [ mn ], noise), where the default values of m and n are both 3, noise is noise in the image, J is the first image, H is the second image;
the covariance matrix G of the PCA method used in step 204 is specifically expressed as follows,
training the sample X i The matrix a of (a) is shown in the following formula,
the feature vector matrix G of the PCA mode after the dimension reduction is shown in the following formula,
feature vector v of each training sample in PCA mode i Projection space construction u of (2) i As shown in the following formula,
wherein lambda is i A feature value representing an i-th feature vector;
RBF kernel function K of RBF-SVM classifier in step 204 above RBF The following formula is shown:
the classification hyperplane of the RBF-SVM classifier is obtained as follows:
f(X i )＝sgn{∑h t y t [K RBF (ν i ，ν j )+b]}
wherein h is t And y is t Respectively representing the classification hyperplane, b being a constant.
3. The face recognition method based on wiener filtering and PCA according to claim 2, wherein the reducing the region of interest of the second image to obtain a third image in step 203 specifically includes the following steps:
step 401, acquiring a first address of a second image, wherein the second image is high and wide;
step 402, opening up a memory buffer area, and initializing the memory buffer area to 255;
step 403, performing binarization processing on the second image, and obtaining a face region through OpenCV;
step 404, tracking boundary points of a face area in the face area, finding a boundary point with a binarization value of 1, and setting R, G, B of the point in a memory buffer area to 255;
step 405, repeating step 404 until the tracking initiation point is returned;
and step 406, copying the content of the memory buffer area into the second image to obtain a third image.
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| CN105117688A (en) * | 2015-07-29 | 2015-12-02 | 重庆电子工程职业学院 | Face identification method based on texture feature fusion and SVM |
| CN108319891A (en) * | 2017-12-07 | 2018-07-24 | 国网新疆电力有限公司信息通信公司 | Face feature extraction method based on sparse expression and improved LDA |
| WO2019080488A1 (en) * | 2017-10-27 | 2019-05-02 | 东南大学 | Three-dimensional human face recognition method based on multi-scale covariance descriptor and local sensitive riemann kernel sparse classification |
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| CN105117688A (en) * | 2015-07-29 | 2015-12-02 | 重庆电子工程职业学院 | Face identification method based on texture feature fusion and SVM |
| WO2019080488A1 (en) * | 2017-10-27 | 2019-05-02 | 东南大学 | Three-dimensional human face recognition method based on multi-scale covariance descriptor and local sensitive riemann kernel sparse classification |
| CN108319891A (en) * | 2017-12-07 | 2018-07-24 | 国网新疆电力有限公司信息通信公司 | Face feature extraction method based on sparse expression and improved LDA |
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