CN114003752B - Database simplification method and system based on particle ball face clustering image quality evaluation - Google Patents
Database simplification method and system based on particle ball face clustering image quality evaluation Download PDFInfo
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Abstract
The invention discloses a database simplification method and a database simplification system based on particle face clustering image quality evaluation, wherein the method comprises the following steps: converting each image in the face database into a vector; inputting a feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors; inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, wherein the face images represented by the points in one particle belong to the same person; dividing the plurality of pellets into a plurality of groups, wherein each group comprises all face images of one person; performing quality evaluation on all face images in each group of pellets to obtain the score of each image; and eliminating the face image with the score smaller than a preset score threshold value to obtain a simplified database. The invention considers factors such as background fuzzy degree, illumination degree and the like to carry out scoring, thereby screening out the image which is most suitable for computer identification, and the simplified database is easy to process and identify.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a database simplification method and a database simplification system based on particle face clustering image quality evaluation.
Background
The global trend of the world is more obvious nowadays, each country has established a face recognition system, the trend of big data is not blocked, and the support of the big data cannot be avoided in the aspects of our lives, such as online shopping, and the shopping platform can roughly analyze the user's favor by clicking and browsing the extracted user on a flat day, so as to recommend the articles the user wants on the home page. The face data is a very important plate in the field of big data, the face recognition technology is almost used in the social life fields such as access control systems and monitoring systems, especially the face payment technology which is more mature in the year, the requirement on the quality of the collected face is higher and higher, if the simplification of a face database cannot be guaranteed and the redundancy of the face database cannot be removed in time, the database is very disordered, and the subsequent processing and recognition become difficult. The existing face database simplifying method does not consider factors such as background fuzzy degree and illumination degree to carry out scoring so as to screen out an image most suitable for computer recognition, and the problem that the simplified database is difficult to recognize is caused.
Disclosure of Invention
The invention aims to solve the technical problems that the existing face database simplification method does not consider factors such as background fuzzy degree and illumination degree to carry out scoring so as to screen out an image which is most suitable for computer recognition, and the simplified database still has the technical problem of difficult recognition.
The invention is realized by the following technical scheme:
a database simplification method based on particle face clustering image quality assessment comprises the following steps:
converting each image in the face database into a vector;
inputting a feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors;
inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, wherein the face images represented by the points in one particle belong to the same person;
dividing the plurality of pellets into a plurality of groups, wherein each group comprises all face images of a person;
performing quality evaluation on all face images in each group of particles to obtain the score of each image;
and eliminating the face images with the scores smaller than a preset score threshold value to obtain a simplified database.
Further, the quality evaluation of all the face images in each group of pellets to obtain the score of each image specifically includes:
and (3) performing quality evaluation on all the face images in each group of the particles by adopting a quality evaluation algorithm BRISQUE to obtain a simplified database.
Further, the quality evaluation of all the face images in each group of pellets by using a quality evaluation algorithm briske to obtain the score of each image specifically comprises:
step one, calculating MSCN coefficient of face image;
step two, fitting the MSCN coefficient into a GGD:
selecting four directions to calculate the MSCN coefficients respectively, namely calculating the current pixel and the four directions of the lower diagonal, the right diagonal, the main diagonal and the secondary diagonal as follows respectively to obtain the four MSCN coefficients;
step four, fitting the four MSCN coefficients into AGGD;
and step five, combining the feature vectors fitted by the GGD and the AGGD, repeating the steps one to four aiming at the face image of 0.5 times, splicing the 36 feature vectors obtained twice to serve as output features, and inputting the output features into the SVM to perform regression to obtain the score of each image.
Further, the plurality of beads are divided into a plurality of groups, each group includes all face images of a person, and the method specifically includes:
the plurality of beads are divided into a plurality of groups according to the bead labels, and each group comprises all face images of one person.
Further, before the step of dividing the plurality of pellets into a plurality of groups, the method further comprises:
and removing other points except the central point in the pellet.
A database simplification system based on particle face clustering image quality assessment comprises:
the conversion module is used for converting each image in the face database into a vector;
the training module is used for inputting the feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors;
the clustering module is used for inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, and the face images represented by the points in one particle belong to the same person;
the grouping module is used for dividing the plurality of pellets into a plurality of groups, and each group comprises all face images of one person;
the quality evaluation module is used for carrying out quality evaluation on all the face images in each group of the particles to obtain the score of each image;
and the first eliminating module is used for eliminating the face images with the scores smaller than a preset score threshold value to obtain a simplified database.
Further, the grouping module is specifically configured to:
the plurality of beads are divided into a plurality of groups according to the bead labels, and each group comprises all face images of one person.
Further, still include:
and the second eliminating module is used for eliminating other points except the central point in the pellets before the pellets are divided into a plurality of groups.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention firstly carries out clustering processing on a face database through a grain sphere neighborhood rough set (GBNRS) grain sphere algorithm, grains have good robustness, most similar feature vectors can be divided together, thereby the face images belonging to the same person are summarized, then all the face images of each person are respectively scored through a non-supervision image quality evaluation (BRISQE) method, the images with poor quality are eliminated, and the images with high quality are reserved, thereby the aim of simplifying the database is achieved, the storage space is saved, the database structure is clear, and the arrangement is clear. By the technical scheme, factors such as background blurring degree and illumination degree are considered for scoring, so that the image which is most suitable for computer recognition is screened out, the existence of a high-quality image is guaranteed, subsequent processing and recognition on the database are not influenced, low-quality images which are difficult to recognize are eliminated, redundancy of the database is eliminated, and the technical problem that the background blurring degree, the illumination degree and other factors are not considered for scoring, so that the image which is most suitable for computer recognition is screened out, and the simplified database still has difficulty in recognition is solved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort. In the drawings:
FIG. 1 is a flow chart of a database reduction method based on shot face cluster image quality assessment according to the present invention;
FIG. 2 is a schematic diagram of a database reduction method based on bead face clustering image quality assessment after initial clustering of beads;
FIG. 3 is a schematic diagram of eliminating points other than a central point in a pellet in the database reduction method based on pellet face cluster image quality evaluation of the present invention;
FIG. 4 is a schematic diagram of a database reduction method based on bead face cluster image quality assessment after bead clustering according to the present invention;
FIG. 5 is a schematic diagram of a schematic structure of a database reduction system based on shot face cluster image quality assessment according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example one
Referring to fig. 1, an embodiment of the present invention provides a database simplification method based on a particle face cluster image quality evaluation, including:
s101, converting each image in a face database into a vector;
s102, inputting a feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors;
s103, inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, wherein the face images represented by the points in one particle belong to the same person;
s104, dividing the plurality of pellets into a plurality of groups, wherein each group comprises all face images of one person;
s105, performing quality evaluation on all face images in each group of pellets to obtain the score of each image;
and S106, eliminating the face image with the score smaller than a preset score threshold value to obtain a simplified database.
As a specific implementation, the quality evaluation of all the face images in each group of pellets to obtain the score of each image specifically includes:
and (3) adopting a quality evaluation algorithm BRISQUE to carry out quality evaluation on all the face images in each group of the pellets to obtain a simplified database.
As a specific implementation manner, the quality evaluation of all face images in each group of pellets by using a quality evaluation algorithm BRISQUE to obtain the score of each image specifically includes:
step one, calculating MSCN coefficient of face image;
step two, fitting the MSCN coefficient into a generalized Gaussian distribution GGD:
selecting four directions to calculate the MSCN coefficients respectively, namely calculating the current pixel and the four directions of the lower diagonal, the right diagonal, the main diagonal and the secondary diagonal as follows respectively to obtain the four MSCN coefficients;
step four, fitting the four MSCN coefficients into an asymmetrical generalized Gaussian distribution AGGD;
and step five, combining the feature vectors fitted by the GGD and the AGGD, repeating the steps one to four aiming at the face image of 0.5 times, splicing the 36 feature vectors obtained twice to serve as output features, and inputting the output features into a Support Vector Machine (SVM) to perform regression so as to obtain the score of each image.
As a specific implementation, the plurality of beads are divided into a plurality of groups, each group includes all face images of a person, and specifically includes:
the plurality of beads are divided into a plurality of groups according to the bead labels, and each group includes all face images of a person.
As a specific implementation manner, before dividing the plurality of pellets into a plurality of groups, the method further includes:
and removing other points except the central point in the pellet.
The clustering algorithm adopted by the embodiment of the invention is a particle neighborhood rough set (GBNRS) particle clustering algorithm, the feature vector of the face is an open source packet from python, the parameter of the face clustering algorithm is the purity of the particles, and the clustering algorithm comprises the following specific steps:
a. converting each image in the face database into a vector, putting the feature matrix with the size of (batch _ size, attribute) into a pooling convolutional network in deep learning for training, and finally obtaining the feature vector with the size of (batch _ size, 10). The embodiment of the invention can adopt a traditional three-layer lenet neural network, the learning rate is 0.01, and the iteration cycle is 100;
b. inputting a feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors; and inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles. Referring to fig. 2, due to the deep learning, the faces of the same category will gradually enter the same bead, and finally the face images represented by the points in the same bead belong to the same person. FIG. 2 includes a plurality of beads, some of which are of the same color, and the faces of the same color beads belong to the same person;
c. the center point of each pellet is selected, and other points except the center point are removed, please refer to fig. 3. Since the closer to the center, the more easily this point is machine recognized, the more representative the feature of this face is the center point than the other points, so eliminating other points in the pellet, the more easily this screened out point is machine recognized.
When the face image is clustered, the particle clustering algorithm is combined with the deep learning method, and because other points except the central point in the particle are removed in the step, the number of the finally separated particles is deviated from the sample number of the previous layer of network, a gradient return function needs to be rewritten, and the gradient of the points in the same particle is set as the gradient of the central point.
Compared with other clustering algorithms, the grain sphere neighborhood rough set (GBNRS) grain sphere clustering algorithm has better robustness, noise (points with low image quality) images can be well eliminated due to the characteristics of the grain spheres, the preliminary screening of a database is equivalently carried out, and accelerated grain sphere codes have higher speed than the similar clustering algorithm and are more suitable for clustering the face database.
The shot clustering algorithm mainly needs to pay attention to the purity of the shots, because some noise needs to be added, although the network identification is good generally, some failures are possible, and even other shots can run into the shot, so that certain noise is set, and the shots can be clustered together although the shots may not be classified correctly. This has the advantage that data of complex shapes can be partitioned and works better than K-means for dense data sets and anomalous data points can be found.
The clustered database is divided into several groups, each group including all the face images of a person, see fig. 4.
The embodiment of the invention adopts a non-supervision image quality evaluation (BRISQE) method, and the quality evaluation algorithm BRISQE is used for carrying out quality evaluation on all face images in each group of pellets to obtain the score of each image. And then eliminating the face image with the score smaller than a preset score threshold value to obtain a simplified database.
The embodiment of the invention firstly carries out clustering processing on the face database through a grain sphere neighborhood rough set (GBNRS) grain sphere algorithm, the grain spheres have good robustness, most similar feature vectors can be divided together, thereby the face images belonging to the same person are summarized, then all the face images of each person are respectively scored through a non-supervision image quality evaluation (BRISQE) method, the images with poor quality are eliminated, and the images with high quality are reserved, thereby the aim of simplifying the database is achieved, the storage space is saved, the structure of the database is clear, and the arrangement is clear. Through the technical scheme, the method and the device provided by the embodiment of the invention consider factors such as background fuzzy degree and illumination degree to score so as to screen out the image which is most suitable for computer recognition, not only ensures the existence of the high-quality image, but also does not influence subsequent processing and recognition of the database, eliminates the low-quality image which is difficult to recognize, eliminates the redundancy of the database, and solves the technical problem that the background fuzzy degree and the illumination degree are not considered in the existing face database simplification method to score so as to screen out the image which is most suitable for computer recognition, so that the simplified database still has difficulty in recognition.
Example two
Referring to fig. 5, an embodiment of the present invention provides a database simplification system based on a particle face cluster image quality evaluation, including:
the conversion module is used for converting each image in the face database into a vector;
the training module is used for inputting the feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors;
the clustering module is used for inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, and the face images represented by the points in one particle belong to the same person;
the grouping module is used for dividing the plurality of the particles into a plurality of groups, and each group comprises all face images of a person;
the quality evaluation module is used for carrying out quality evaluation on all the face images in each group of particles to obtain the score of each image;
and the first eliminating module is used for eliminating the face images with the scores smaller than a preset score threshold value to obtain a simplified database.
As a specific implementation, the grouping module is specifically configured to:
the plurality of beads are divided into a plurality of groups according to the bead labels, and each group comprises all face images of one person.
As a specific embodiment, the method further comprises the following steps:
and the second eliminating module is used for eliminating other points except the central point in the pellets before the pellets are divided into a plurality of groups.
For the specific implementation process of the system described in the second embodiment, since the method in the first embodiment has been described in detail, it is not described herein again.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A database simplification method based on particle face clustering image quality assessment is characterized by comprising the following steps:
converting each image in the face database into a vector;
inputting a feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors;
inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, wherein the face images represented by the points in one particle belong to the same person;
dividing the plurality of pellets into a plurality of groups, wherein each group comprises all face images of one person;
performing quality evaluation on all face images in each group of particles to obtain the score of each image;
and eliminating the face images with the scores smaller than a preset score threshold value to obtain a simplified database.
2. The database reduction method based on the particle face clustering image quality assessment according to claim 1,
the quality evaluation of all the face images in each group of pellets to obtain the score of each image specifically comprises the following steps:
and (3) adopting a quality evaluation algorithm BRISQUE to carry out quality evaluation on all the face images in each group of the pellets to obtain a simplified database.
3. The database reduction method based on the particle face clustering image quality assessment according to claim 2,
the quality evaluation method BRISQUE is adopted to carry out quality evaluation on all face images in each group of particles to obtain the score of each image, and the method specifically comprises the following steps:
step one, calculating MSCN coefficient of the face image;
step two, fitting the MSCN coefficient into a GGD:
selecting four directions to calculate the MSCN coefficients respectively, namely calculating the current pixel and the four directions of the lower diagonal, the right diagonal, the main diagonal and the secondary diagonal as follows respectively to obtain the four MSCN coefficients;
step four, fitting the four MSCN coefficients into AGGD;
and step five, combining the feature vectors fitted by the GGD and the AGGD, repeating the steps one to four aiming at the face image of 0.5 times, splicing the 36 feature vectors obtained twice to serve as output features, and inputting the output features into the SVM to perform regression to obtain the score of each image.
4. The database reduction method based on the image quality evaluation of the particle face clusters according to claim 1,
dividing the plurality of pellets into a plurality of groups, wherein each group comprises all face images of a person, and the method specifically comprises the following steps:
the plurality of beads are divided into a plurality of groups according to the bead labels, and each group comprises all face images of one person.
5. The database reduction method based on the particle face clustering image quality assessment according to claim 1,
before the plurality of pellets are divided into a plurality of groups, the method further comprises the following steps:
and removing other points except the central point in the pellet.
6. A database simplification system based on particle face clustering image quality assessment is characterized by comprising:
the conversion module is used for converting each image in the face database into a vector;
the training module is used for inputting the feature matrix formed by the vectors into a deep learning model for training to obtain a plurality of feature vectors;
the clustering module is used for inputting the plurality of feature vectors into a particle model for clustering to form a plurality of particles, and the face images represented by the points in one particle belong to the same person;
the grouping module is used for dividing the plurality of the particles into a plurality of groups, and each group comprises all face images of a person;
the quality evaluation module is used for carrying out quality evaluation on all the face images in each group of the particles to obtain the score of each image;
and the first eliminating module is used for eliminating the face images with the scores smaller than a preset score threshold value to obtain a simplified database.
7. The database reduction system for image quality evaluation based on particle face clustering according to claim 6,
the grouping module is specifically configured to:
the plurality of beads are divided into a plurality of groups according to the bead labels, and each group comprises all face images of one person.
8. The database reduction system based on particle face clustering image quality assessment according to claim 6, further comprising:
and the second eliminating module is used for eliminating other points except the central point in the granules before the granules are divided into a plurality of groups.
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| CN111126169B (en) * | 2019-12-03 | 2022-08-30 | 重庆邮电大学 | Face recognition method and system based on orthogonalization graph regular nonnegative matrix factorization |
| CN111144366A (en) * | 2019-12-31 | 2020-05-12 | 中国电子科技集团公司信息科学研究院 | Strange face clustering method based on joint face quality assessment |
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