CN111340111A - Method for recognizing face image set based on wavelet kernel extreme learning machine - Google Patents
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Abstract
The invention discloses a method for recognizing a face image set based on a wavelet kernel extreme learning machine, which comprises the following steps: s1, inputting an image set; s2, preprocessing the image set; s3, modeling the image set: initializing a global wavelet kernel limit learning machine model; s4, training the global wavelet kernel limit learning machine model by using the training image set to obtain a wavelet kernel limit learning machine model of each type of image set; s5, respectively reconstructing the original test image set by using the wavelet kernel limit learning machine model corresponding to each type of image set, and outputting the reconstructed test image set; s6, calculating a reconstruction error between the reconstructed test image set and the original test image set; s7, obtaining the minimum reconstruction error, obtaining the category to which the minimum reconstruction error belongs, wherein the category to which the minimum reconstruction error belongs represents the category represented by the test image set; and S8, outputting the category to which the test image set belongs. The invention can efficiently realize the classification of the face image set, and has high learning speed and good generalization performance.
Description
Technical Field
The invention relates to the field of face image set identification, in particular to a method for identifying a face image set based on a wavelet Kernel Extreme Learning Machine (KELM).
Background
With the development of artificial intelligence, face recognition develops rapidly, which is particularly reflected in that from the initial single face image recognition to the nearest face image set recognition, the face has various angles and illumination changes due to video or photographing, so that the attention points of face recognition change. Image set identification may describe individuals in more detail. Meanwhile, the face image set identification also faces many challenges, such as how to define the distance between two individual face image sets and how to extract more effective individual features.
A series of studies on face image set recognition have appeared in recent decades, and one of the main difficulties is how to effectively model and extract features that can effectively express individuals. The existing methods greatly improve the recognition rate of the face image set, such as methods of establishing manifold models, establishing manifold subspaces and the like. Hu et al define the distance between sets as the distance between their Sparse Approximate Nearest Points (SANPs). For the representation of the image set on the manifold, suitable distance measures are used, such as geodesic distances and projection kernel measures on the Grassmann manifold, and log-mapped distance measures on the riemann manifold. However, the method of manifold modeling of an image requires that the manifold class of a face image be presupposed on the manifold of the image in advance. But it is assumed that the face data follows a gaussian distribution, such as Wang and Chen, which proposes Manifold Discriminant Analysis (MDA), modeling each image set with multiple local linear clusters; wang et al directly uses modeling of covariance matrices for each image set. They map the covariance matrix of each image set from the Riemann manifold to Euclidean space through a kernel function based on Log Euclidean distances. The image set is then classified according to learning a regression function using a kernel partial least squares method. In practice this does not apply to all images. Or other methods require the prior assumption that the image set can be represented by a linear subspace, such as Harandi et al model the image set of the linear subspace as points on a grassmann manifold. They define kernel functions that map the image set from the grassmann manifold to the euclidean space, where classification is by Graph Embedding Discriminant Analysis (GEDA); hayat et al learn the structure of each image set using a deep learning model. The labels of the test set are then estimated according to the minimum reconstruction error and the majority voting scheme. In general, structure-based algorithms require relatively more images in each set to accurately model the data structure. However, data may be located on complex manifolds, and to model more complex data structures, many methods have also been proposed to model image sets as convex or affine hulls of data samples.
Since the prior art is conceptually similar to the nearest neighbor classification (KNN) and certain constraints must be imposed to avoid finding neighboring points in certain low-dimensional spaces where the image sets may intersect. However, modeling more complex image set structures comes at the cost of increased algorithm complexity. Therefore, these algorithms cannot efficiently handle the large image set classification task. Therefore, it is necessary to develop a method for recognizing a face image set based on a wavelet Kernel Extreme Learning Machine (KELM).
Disclosure of Invention
The invention aims to provide a method for recognizing a face image set based on a wavelet Kernel Extreme Learning Machine (KELM), which realizes the classification and recognition of the face image set by using the KELM and can efficiently realize the classification of the face image set on the premise of not assuming a data structure in advance.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a face image set recognition method based on a wavelet kernel limit learning machine comprises the following steps: s1, inputting an image set, wherein the image set is divided into a plurality of image sets; s2, preprocessing the input image set; s3, modeling the image set: initializing a global wavelet kernel limit learning machine model; s4, training the global wavelet kernel limit learning machine model by using the training image set to obtain a trained wavelet kernel limit learning machine model of each type of image set; s5, after obtaining the wavelet kernel limit learning machine model of each type of image set after training, respectively reconstructing the original test image set before reconstruction by using the wavelet kernel limit learning machine model after training corresponding to each type of image set, and outputting the reconstructed test image set; s6, calculating a reconstruction error between the reconstructed test image set and the original test image set; s7, acquiring the minimum reconstruction error, and acquiring the category to which the minimum reconstruction error belongs, wherein the category to which the minimum reconstruction error belongs represents the category represented by the test image set; and S8, outputting the category to which the test image set belongs.
Preferably, the pre-treatment comprises: graying of an image: the graying process means that the RGB values of each pixel point are unified into the same value, and the grayed image is changed into a single channel from three channels; feature extraction: and extracting the characteristic vector of the grayed face image by using an HOG-NMF method.
Preferably, the training image set is selected from at least a portion of the input image set, the original test image set being the remaining portion of the input image set; the input image set is divided into N classes, and each class of training image set is marked asEach type of test image is collected asWherein d represents the characteristic dimension of each type of face image in the training image set or the test image set, and i represents the ith type of face.
Preferably, the step S3 further includes: defining an initial global wavelet kernel limit learning machine KELM model, and recording the weight of the adopted global KELM model asWherein h represents the number of hidden nodes;and representing the weight of the ith hidden node of the human face image set G in the global wavelet kernel limit learning machine model.
Preferably, the step S4 further includes: taking the face image of each type of test image set as the input of an initial global wavelet kernel limit learning machine and performing comparisonTraining is carried out, and a wavelet kernel limit learning machine model of each type of image set after training is obtained and recorded asLjA wavelet kernel limit learning machine model representing the jth class face image set, wherein the weight of the jth class wavelet kernel limit learning machine model is defined as
Preferably, in step S5, each test image set isModels using respective classes of image setsRespectively reconstructing: taking each type of face image set in the test image set as corresponding each wavelet core extreme learning machine modelRespectively obtaining N reconstructed test image sets and recording the N reconstructed test image sets as N wavelet core extreme learning machine models obtained by training
Preferably, the step S6 further includes: calculating the reconstruction error between the test image set before reconstruction and the test image set after reconstruction as a squared Euclidean distance:
wherein, i represents the ith class of image,set of images representing the ith class before reconstructionAnd the reconstructed image setThe squared euclidean distance between.
Preferably, the step S7 further includes: test image setClass Y of (2) depends on the minimum reconstruction errorThe reconstruction error is minimum, the closer to a certain class, the test image class label is obtained
Compared with the prior art, the invention has the beneficial effects that: the invention provides a novel image set identification method, which uses a wavelet kernel extreme learning machine to identify a face image set, although the image set has nonlinear characteristics, the method does not need a plurality of assumptions of a data set structure or select an algorithm related to a structure; in addition, the kernel extreme learning machine has the characteristics of high learning speed and good generalization performance.
Drawings
FIG. 1 is a flow chart of a method for identifying a face image set according to the present invention;
fig. 2-3 are schematic diagrams illustrating the principle of training and testing a face image set by using a wavelet kernel extreme learning machine according to the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-3, the present invention provides a method for recognizing a face image set based on a wavelet Kernel Extreme Learning Machine (KELM), the method comprising the following steps:
s1, inputting an image set; classifying an input image set, and selecting images in the input image set in a testing process to serve as a training image set and a testing image set of a subsequent step; for example, the classification here is classified according to a plurality of face types, for example, the image set includes N persons, each person includes a plurality of image sets, and the number of classes of the classification may be considered to be N at this time, that is, the input image set is divided into N types of face image sets.
S2, preprocessing the image set input in the step S1;
s3, modeling the image set: initializing a global wavelet kernel limit learning machine model;
s4, training the global wavelet kernel limit learning machine model by using a training image set, and respectively obtaining a trained wavelet kernel limit learning machine model of each type of image set;
s5, after obtaining the trained wavelet kernel limit learning machine model of each type of image set, respectively reconstructing the test image set (the original test image set before reconstruction) by using the wavelet kernel limit learning machine model of each type of image set, and outputting the reconstructed test image set;
s6, calculating the reconstruction error between the output reconstructed test image set and the original test image set;
s7, obtaining the minimum reconstruction error and obtaining the category to which the minimum reconstruction error belongs; wherein the category to which the minimum reconstruction error belongs represents a category represented by the test image set;
and S8, outputting the category to which the test image set belongs.
In step S1, the method further includes:
(1.1) using an ORL image set for recognition, the ORL data set comprising N (e.g., 40) individuals, each person having M (e.g., M ═ 10) images;
(1.2) randomly selecting M1 (for example, M1 ≦ 6, and M1 ≦ M) images of each person as a training image set (training set), so that there are N × M1 (for example, N × M1 ≦ 40 × 6 ≦ 240) training images, where each training image set is denoted asIllustratively, when the image set contains 40 types of face image sets, each type of training image set is recorded asd represents the characteristic dimension of each type of face image in the training set, and i represents the face of the ith type.
(1.3) the remaining M2 (M2-M1, e.g., 10-6-4) images of each person are selected as a test image set (test set for short), and then N × M2 (e.g., N × M2-40 × 4-160) test images are available, where each test image set is designated as a test image setIllustratively, when the image set contains 40 types of face image sets, each type of test image set is marked asd represents the characteristic dimension of each type of face image in the test set, and i represents the face of the ith type.
(1.4) firstly, using the wavelet kernel extreme learning machine after random weight parameters as an initial model, and after the initial model is available, using the training sets to train the weight parameters of different training sets to obtain models of the wavelet kernel extreme learning machine of different training sets; and the test set is used to test the accuracy of the current model.
In step S2, the preprocessing of the face image set, such as graying the image and extracting features, further includes the following steps:
the method comprises the steps of preprocessing a face image set, namely graying the face, wherein the graying process means that RGB values of each pixel point are unified into the same value, an image after graying is changed from three channels into a single channel, data processing of the single channel is simpler than that of the three channels, then characteristic vectors are extracted from the face image after graying by using a HOG-NMF (Histogram of oriented Gradient-Non-negative Matrix Factorization) method (namely, after the characteristic vectors are extracted by using the HOG, the characteristics which can most represent the face are extracted by using the NMF method), so that characteristic dimensions are reduced, the calculation complexity is reduced, the calculation speed is improved, and the identification method is more accurate.
In step S3, modeling the preprocessed face image set, further includes the following steps:
firstly, an initial global wavelet kernel limit learning machine model (KELM model) is defined, and the global KELM model weight L is usedGIs marked ash represents the number of hidden nodes; the model comprises an input layer, a hidden layer and an output layer, wherein the weights between the input layer and the hidden layer are initialized randomly, and the weights before the hidden layer and the output layer are initialized randomly; wherein,and representing the weight of the ith hidden node of the human face image set G in the global wavelet kernel limit learning machine model.
The step S4 further includes: the face image of each type of test image set is used as the input of the initial global wavelet core limit learning machine model, the training is carried out on the face image, the trained wavelet core limit learning machine model of each type of image set is obtained respectively, and the model is marked asWherein L isjA wavelet kernel limit learning machine model representing the jth class face image set, wherein the weight of the jth class wavelet kernel limit learning machine model is defined as
In a specific embodiment, the training image set includes 40 classes of face image sets, N is 40, and each class includes 6 face images (i.e. as a test image set of each class), the face images of each class of test image set are used as the input of an initial global wavelet kernel limit learning machine, a gradient descent method is used to minimize a loss function until the loss function cannot be descended, a wavelet kernel limit learning machine model of each class of face image set is obtained, and due to the existence of 40 classes of face images, the number of the wavelet kernel limit learning machine models is 40, and these models are recorded as the models
In step S5, the method further includes:
each type of test image setModel using corresponding class of face image setRespectively reconstructing, namely taking each type of human face image set in the test image set as each wavelet kernel limit learning machine modelThe input of (1) obtaining N wavelet kernel extreme learning machine models after modeling, and respectively obtaining N outputs after model operation, namely N reconstructed test image sets which are recorded asI.e. to the original test image setCan be obtained after reconstructionNamely, it isThroughObtaining correspondences after model reconstruction
As a specific embodiment, the image set comprises a 40-class face image set, and if N is 40, the image set is testedModel using corresponding each type of face image setRespectively reconstructing, namely taking each type of human face image set in the test image set as each wavelet kernel limit learning machine modelBecause 40 wavelet kernel extreme learning machine models are obtained after modeling and training, after model operation, each face image set can obtain one output, and 40 types of faces can obtain 40 outputs and are recorded asThe reconstructed test image set is obtained
In step S6, the method further includes:
to test the sampleThe reconstruction error of (2) is calculated as the squared euclidean distance:i represents the image of the i-th class,set of images representing the ith class before reconstructionAnd the reconstructed image setThe squared euclidean distance between. Each image set in the test image set has 4 images, for example, the reconstructed output of one image set and the non-reconstructed test image set are calculated by calculating the squared Euclidean distanceSimilarly, reconstruction errors can be obtained after each type of image is reconstructed
In step S7, the method further includes:
test specimenClass Y of (2) depends on the minimum reconstruction errorAccording to distanceJudging which classification the test set belongs to, wherein the judgment is carried out according to the minimum reconstruction error: the error is minimized, namely the closer to a certain class, the test image class label is obtained
In step S8, the method further includes:
reconstruction errorThe smallest of them is recorded asAnd further obtaining a category i to which the image of the test set belongs, wherein the category i represents which category the image of the test set belongs to, namely completing classification, and identifying the category to which the test face image set belongs.
In the present invention, the algorithm of the kernel limit learning machine for face image set classification is as follows:
inputting:
the face image set G comprises N types of faces, and each type of face image set isClass label is Y ═ Ytrain}NTesting a set of human face images as
(II) outputting: test set XtestClass Y to which it belongs;
(III) training:
for j=1:N do
end for
and (IV) testing:
for j=1:N do
(wherein,LjA wavelet kernel-limit learning model representing a j-th class of face image set,representing the ith class of image set in the test image set,to representPassing through a model LjOutputting the image set obtained after the reconstruction,wavelet kernel formula representing a set of images);
end for
end for
in summary, the image set identification method provided by the invention uses a wavelet kernel limit learning machine to identify a face image set, and although the image set has nonlinear characteristics, the method provided by the invention does not need many assumptions of the structure of the data set or selects an algorithm related to the structure; in addition, the kernel extreme learning machine has the characteristics of high learning speed and good generalization performance.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (8)
1. A face image set recognition method based on a wavelet kernel limit learning machine is characterized by comprising the following steps:
s1, inputting an image set, wherein the image set is divided into a plurality of image sets;
s2, preprocessing the input image set;
s3, modeling the image set: initializing a global wavelet kernel limit learning machine model;
s4, training the global wavelet kernel limit learning machine model by using the training image set to obtain a trained wavelet kernel limit learning machine model of each type of image set;
s5, after obtaining the wavelet kernel limit learning machine model of each type of image set after training, respectively reconstructing the original test image set before reconstruction by using the wavelet kernel limit learning machine model after training corresponding to each type of image set, and outputting the reconstructed test image set;
s6, calculating a reconstruction error between the reconstructed test image set and the original test image set;
s7, acquiring the minimum reconstruction error, and acquiring the category to which the minimum reconstruction error belongs, wherein the category to which the minimum reconstruction error belongs represents the category represented by the test image set;
and S8, outputting the category to which the test image set belongs.
2. The method for recognizing a face image set based on a wavelet kernel limit learning machine according to claim 1,
the pretreatment comprises the following steps:
graying of an image: the graying process means that the RGB values of each pixel point are unified into the same value, and the grayed image is changed into a single channel from three channels;
feature extraction: and extracting the characteristic vector of the grayed face image by using an HOG-NMF method.
3. The method for recognizing a face image set based on a wavelet kernel limit learning machine according to claim 1,
the training image set is selected from at least a portion of the input image set, the original test image set being the remaining portion of the input image set;
4. The method for recognizing a face image set based on a wavelet kernel limit learning machine according to claim 3,
the step S3 further includes:
defining an initial global wavelet kernel limit learning machine KELM model, and recording the weight of the adopted global KELM model asWherein h represents the number of hidden nodes;and representing the weight of the ith hidden node of the human face image set G in the global wavelet kernel limit learning machine model.
5. The method for recognizing a face image set based on a wavelet kernel limit learning machine according to claim 4,
the step S4 further includes: each one is to beThe human face image of the class test image set is used as the input of the initial global wavelet core limit learning machine model and trained, and the trained wavelet core limit learning machine model of each class of image set is obtained and recorded asLjA wavelet kernel limit learning machine model representing the jth class face image set, wherein the weight of the jth class wavelet kernel limit learning machine model is defined as
6. The method for recognizing a face image set based on a wavelet kernel limit learning machine according to claim 5,
in the step S5, each type of test image setModels using respective classes of image setsRespectively reconstructing: taking each type of face image set in the test image set as corresponding each wavelet core extreme learning machine modelRespectively obtaining N reconstructed test image sets and recording the N reconstructed test image sets as N wavelet core extreme learning machine models obtained by training
7. The method for recognizing a face image set based on a wavelet kernel limit learning machine according to claim 6,
the step S6 further includes:
calculating the reconstruction error between the test image set before reconstruction and the test image set after reconstruction as a squared Euclidean distance:
8. The method for recognizing a set of human faces based on a wavelet kernel limit learning machine according to claim 7,
the step S7 further includes:
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