CN110211094B - Intelligent judging method and device for black eye and computer readable storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses an intelligent judging method for black eye, which comprises the following steps: receiving a face image set, wherein the face image set is divided into a positive sample set and a negative sample set through a label set, the positive sample set and the negative sample set are preprocessed, the label set is input into a black eye judgment model, the data of the positive sample set and the negative sample set are subjected to direction gradient direct operation and then are input into the black eye judgment model, the black eye judgment model is trained based on a support vector machine algorithm, and training is stopped until a loss function value in the support vector machine algorithm is smaller than a threshold value; and receiving a test set of the user to the black eye judgment model to judge whether the black eye exists or not, and outputting a result. The invention also provides an intelligent judging device for the black eye and a computer readable storage medium. The invention can realize the accurate intelligent judging function of the black eye.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for automatically and intelligently judging black eyes based on face data input and a computer readable storage medium.
Background
The black eye is caused by night stay, large mood fluctuation, eye fatigue and aging, which cause the blood flow velocity of blood vessels of the skin of eyes to be too slow to form stagnation, insufficient oxygen supply of tissues, excessive accumulation of metabolic wastes in blood vessels and eye pigmentation. The older the person, the thinner the subcutaneous fat around the eyes becomes, so the more visible is the dark circles. In the current society, many people have black eyes but are not self-aware, so that whether the black eyes exist is judged, however, the accurate identification of the black eyes has many problems, such as the majority of application scenes are complex, the difficulty in identification is increased due to the local dynamic change of the background, the target shadow caused by uneven illumination and the like, in addition, the face is a non-rigid target and has rich gesture characteristics, and different gestures of the same face often have large differences in detection and identification.
Disclosure of Invention
The invention provides an intelligent judging method and device for black eyes and a computer readable storage medium, and mainly aims to provide a scheme for realizing an accurate intelligent judging function for black eyes.
In order to achieve the above object, the invention provides an intelligent judging method for black eye, comprising the following steps:
the data receiving layer receives a face image set, the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operations comprising graying, binarizing and noise reduction are carried out on the positive sample set and the negative sample set, and the positive sample set, the negative sample set and the label set which are subjected to preprocessing are input to the data processing layer;
the data processing layer receives the positive sample set, the negative sample set and the label set which are subjected to pretreatment, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and then inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training;
receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result.
Optionally, the data in the positive sample set is a face including dark circles, and the data in the negative sample set is a face not including dark circles.
Optionally, the noise reduction processing adopts an adaptive image noise reduction filtering method:
g(x，y)＝η(x，y)+f(x，y)
wherein (x, y) represents the coordinates of the pixel point of the image, f(x, y) is output data obtained by denoising the positive sample set and the negative sample set based on the adaptive image denoising filter method, η (x, y) is noise, g (x, y) is the positive sample set and the negative sample set,
for the total noise variance of the positive and negative sample sets, +.>
Is the pixel gray average value of the (x, y), is->
For the pixel gray variance of (x, y), L represents the current pixel point coordinates.
Optionally, the data of the positive sample set and the negative sample set are input to a black eye judgment model after being subjected to a direct gradient square operation, including:
calculating gradient amplitude values and gradient direction values of all pixel points (x, y) of data in the face image set, and forming a gradient matrix by taking the gradient amplitude values as first components and the gradient direction values as second components;
dividing the data in the gradient matrix into a plurality of small blocks, adding the gradient amplitude value and the gradient direction value of each small block, connecting the added values in series to form a gradient direction straight feature, and inputting the gradient direction straight feature into a black eye judgment model.
Optionally, the support vector machine algorithm includes a nonlinear mapping and constraint solving;
the nonlinear mapping is:
κ(x i ，x j )＝<θ(x i )，θ(x j )>
wherein,,<θ(x i )，θ(x j )>represents the gradient direction square characteristic (x i ，x j ) Inner product calculation of nonlinear mapping, κ (x i ，x j ) For the gradient direction square feature (x i ，x j ) Is a non-linear mapping function of (2);
the constraint solution is as follows:
Wherein m is the number of the gradient direction rectangularity features, alpha i ，α j Lagrangian number multiplied by a factor, y, solved for the constraint i ，y j Labels for the positive and negative samples, s.t, are constraints.
In addition, in order to achieve the above object, the present invention also provides an intelligent black eye judging device, which includes a memory and a processor, wherein the memory stores an intelligent black eye judging program capable of running on the processor, and the intelligent black eye judging program when executed by the processor implements the following steps:
the data receiving layer receives a face image set, the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operations comprising graying, binarizing and noise reduction are carried out on the positive sample set and the negative sample set, and the positive sample set, the negative sample set and the label set which are subjected to preprocessing are input to the data processing layer;
the data processing layer receives the positive sample set, the negative sample set and the label set which are subjected to pretreatment, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and then inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training;
receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result.
Optionally, the data in the positive sample set is a face including dark circles, and the data in the negative sample set is a face not including dark circles.
Optionally, the noise reduction processing adopts an adaptive image noise reduction filtering method:
g(x，y)＝η(x，y)+f(x，y)
wherein (x, y) represents coordinates of pixel points of an image, f (x, y) is output data obtained by performing noise reduction processing on the positive sample set and the negative sample set based on the adaptive image noise reduction filtering method, eta (x, y) is noise, g (x, y) is the positive sample set and the negative sample set,
for the total noise variance of the positive and negative sample sets, +.>
Is the pixel gray average value of the (x, y), is->
For the pixel gray variance of (x, y), L represents the current pixel point coordinates.
Optionally, the data of the positive sample set and the negative sample set are input to a black eye judgment model after being subjected to a direct gradient square operation, including:
calculating gradient amplitude values and gradient direction values of all pixel points (x, y) of data in the face image set, and forming a gradient matrix by taking the gradient amplitude values as first components and the gradient direction values as second components;
dividing the data in the gradient matrix into a plurality of small blocks, adding the gradient amplitude value and the gradient direction value of each small block, connecting the added values in series to form a gradient direction straight feature, and inputting the gradient direction straight feature into a black eye judgment model.
Optionally, the support vector machine algorithm includes a nonlinear mapping and constraint solving;
the nonlinear mapping is:
κ(x i ，x j )＝<θ(x i )，θ(x j )>
wherein < θ (x i )，θ(x j )>Represents the gradient direction square characteristic (x i ，x j ) Inner product calculation of nonlinear mapping, κ (x i ，x j ) For the gradient direction square feature (x i ，x j ) Is a non-linear mapping function of (2);
the constraint solution is as follows:
Wherein m is the number of the gradient direction rectangularity features, alpha i ，α j Lagrangian number multiplied by a factor, y, solved for the constraint i ，y j Labels for the positive and negative samples, s.t, are constraints.
In addition, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a black eye intelligent judgment program executable by one or more processors to implement the steps of the black eye intelligent judgment method as described above.
The invention provides an intelligent judging method and device for black eyes and a computer readable storage medium, wherein a data receiving layer receives a face image set, the face image set is divided into a positive sample set and a negative sample set by a label set, and the positive sample set, the negative sample set and the label set are input to a data processing layer; the data processing layer receives the positive sample set, the negative sample set and the label set which are subjected to pretreatment, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and then inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training; receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result. The invention has the advantages that the support vector machine model with higher use efficiency is used, and the noise affecting the judgment of the model is reduced based on a plurality of data preprocessing methods in the early stage, so that the invention can realize the accurate intelligent judgment function of the black eye.
Drawings
Fig. 1 is a flow chart of an intelligent judging method for black eye according to an embodiment of the invention;
fig. 2 is a schematic diagram of an internal structure of an intelligent judging device for black eye according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of an intelligent judging procedure for black eye in the intelligent judging device for black eye according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides an intelligent judging method for black eye. Referring to fig. 1, a flow chart of an intelligent judging method for black eye according to an embodiment of the invention is shown. The method may be performed by an apparatus, which may be implemented in software and/or hardware.
In this embodiment, the method for intelligently judging the black eye includes: (step according to the corresponding modification of the claims)
S1, a data receiving layer receives a face image set, the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operations comprising graying, binarizing and noise reduction are carried out on the positive sample set and the negative sample set, and then the positive sample set, the negative sample set and the label set which are subjected to preprocessing are input to a data processing layer.
In the preferred embodiment of the present invention, the data in the positive sample set is a face including dark circles, and the data in the negative sample set is a face not including dark circles.
In a preferred embodiment of the present invention, the graying operation is to convert the data in the positive sample set and the negative sample from RGB format to black-and-white gray format by using a scaling method. The ratio method is as follows: acquiring R, G, B pixel values of each pixel point in the positive sample set and the negative sample, and converting the pixel points into a black-white gray format according to the following function:
0.30*R+0.59*G+0.11*B。
the binarization operation includes setting a threshold value first, when a pixel in the black-and-white gray format is larger than the threshold value, the pixel becomes 255, and when the pixel in the black-and-white gray format is smaller than the threshold value, the pixel becomes 0, that is, the black-and-white format indicates that the pixel values of the positive sample set and the negative sample set are 0 or 255.
The noise reduction is to perform noise reduction processing on the black-and-white format data based on an adaptive image noise reduction filtering method, and the adaptive image noise reduction filtering method is as follows:
g(x，y)＝η(x，y)+f(x，y)
wherein (x, y) represents the coordinates of the pixel points of the image, and f (x, y) represents the processing of the black-and-white format data based on the adaptive image noise reduction filtering methodThe output data after the line noise reduction processing, eta (x, y) is noise, g (x, y) is the black-and-white format data,
for the noise total variance of the black and white format data, < >>
Is the pixel gray average value of the (x, y), is->
For the pixel gray variance of (x, y), L represents the current pixel point coordinates.
S2, the data processing layer receives the positive sample set and the negative sample set which are subjected to pretreatment and the label set, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training.
In a preferred embodiment of the present invention, the data of the positive sample set and the negative sample set are input to the black eye judgment model after being subjected to a direction gradient direct operation, including: calculating gradient amplitude values and gradient direction values of all pixel points (x, y) of data in the face image set, and forming a gradient matrix by taking the gradient amplitude values as first components and the gradient direction values as second components; dividing the data in the gradient matrix into a plurality of small blocks, calculating the sum of the gradient amplitude and the gradient direction value of each small block, and inputting the sum in series to the black eye judgment model to form a gradient direction straight feature.
The support vector machine algorithm in the preferred embodiment of the invention comprises nonlinear mapping and constraint solving;
the nonlinear mapping is:
κ(x i ，x j )＝<θ(x i )，θ(xj)>
wherein,,<θ(x i )，θ(x j )>represents the gradient direction square characteristic (x i ，x j ) Inner product calculation of nonlinear mapping, κ (x i ，x j ) For the gradient direction square feature (x i ，x j ) Is a non-linear mapping function of (2);
the constraint solution is as follows:
Wherein m is the number of the gradient direction rectangularity features, alpha i ，α j Lagrangian number multiplied by a factor, y, solved for the constraint i ，y j Labels for the positive and negative samples, s.t, are constraints.
In the preferred embodiment of the present invention, the loss function is a least square method, and the loss function value is L (e):
wherein e is the error value between the training value of the black eye judgment model and the label set, k is the total number of the positive sample set and the negative sample set, y i For the tag set, y' i For the training value, the threshold is typically set to 0.01.
S3, receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result.
The data mapping in the preferred embodiment of the invention adopts the nonlinear mapping method of the support vector machine.
The invention also provides an intelligent judging device for the black eye. Referring to fig. 2, an internal structure diagram of an intelligent judging device for black eye according to an embodiment of the invention is shown.
In this embodiment, the black eye intelligent judging device 1 may be a PC (Personal Computer ), or a terminal device such as a smart phone, a tablet computer, a portable computer, or a server. The black eye intelligent judging device 1 at least comprises a memory 11, a processor 12, a communication bus 13 and a network interface 14.
The memory 11 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the black eye smart determination device 1, for example a hard disk of the black eye smart determination device 1. The memory 11 may also be an external storage device of the black eye Smart determination apparatus 1 in other embodiments, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like provided on the black eye Smart determination apparatus 1. Further, the memory 11 may also include both an internal memory unit and an external memory device of the black eye intelligent judging apparatus 1. The memory 11 may be used not only for storing application software installed in the black eye intelligent judging apparatus 1 and various types of data, for example, codes of the black eye intelligent judging program 01 and the like, but also for temporarily storing data that has been output or is to be output.
The processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in the memory 11, for example for executing the black eye intelligent judging program 01, etc.
The communication bus 13 is used to enable connection communication between these components.
The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), typically used to establish a communication connection between the apparatus 1 and other electronic devices.
Optionally, the device 1 may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-emitting diode) touch, or the like. The display may also be referred to as a display screen or a display unit, as appropriate, for displaying information processed in the black eye intelligent judgment device 1 and for displaying a visual user interface.
Fig. 2 shows only the black eye intelligent judging device 1 having the components 11-14 and the black eye intelligent judging program 01, and it will be understood by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the black eye intelligent judging device 1, and may include fewer or more components than shown, or may combine some components, or may be a different arrangement of components.
In the embodiment of the device 1 shown in fig. 2, the memory 11 stores therein an intelligent judgment program 01 for black eye; the processor 12 performs the following steps when executing the black eye intelligent judging program 01 stored in the memory 11:
step one, a data receiving layer receives a face image set, wherein the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operations comprising graying, binarizing and noise reduction are carried out on the positive sample set and the negative sample set, and then the positive sample set, the negative sample set and the label set which are subjected to preprocessing are input to a data processing layer.
In the preferred embodiment of the present invention, the data in the positive sample set is a face including dark circles, and the data in the negative sample set is a face not including dark circles.
In a preferred embodiment of the present invention, the graying operation is to convert the data in the positive sample set and the negative sample from RGB format to black-and-white gray format by using a scaling method. The ratio method is as follows: acquiring R, G, B pixel values of each pixel point in the positive sample set and the negative sample, and converting the pixel points into a black-white gray format according to the following function:
0.30*R+0.59*G+0.11*B
the binarization operation includes setting a threshold value first, when a pixel in the black-and-white gray format is larger than the threshold value, the pixel becomes 255, and when the pixel in the black-and-white gray format is smaller than the threshold value, the pixel becomes 0, that is, the black-and-white format indicates that the pixel values of the positive sample set and the negative sample set are 0 or 255.
The noise reduction is to perform noise reduction processing on the black-and-white format data based on an adaptive image noise reduction filtering method, and the adaptive image noise reduction filtering method is as follows:
g(x，y)＝η(x，y)+f(x，y)
wherein (x, y) represents coordinates of pixel points of an image, f (x, y) is output data obtained by performing noise reduction processing on the black-and-white format data based on an adaptive image noise reduction filtering method, eta (x, y) is noise, g (x, y) is the black-and-white format data,
for the noise total variance of the black and white format data, < >>
Is the pixel gray average value of the (x, y), is->
For the pixel gray variance of (x, y), L represents the current pixel point coordinates.
Step two, the data processing layer receives the positive sample set and the negative sample set which are subjected to pretreatment and the label set, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training.
In a preferred embodiment of the present invention, the data of the positive sample set and the negative sample set are input to the black eye judgment model after being subjected to a direction gradient direct operation, including: calculating gradient amplitude values and gradient direction values of all pixel points (x, y) of data in the face image set, and forming a gradient matrix by taking the gradient amplitude values as first components and the gradient direction values as second components; dividing the data in the gradient matrix into a plurality of small blocks, calculating the sum of the gradient amplitude and the gradient direction value of each small block, and inputting the sum in series to the black eye judgment model to form a gradient direction straight feature.
The support vector machine algorithm in the preferred embodiment of the invention comprises nonlinear mapping and constraint solving;
the nonlinear mapping is:
κ(x i ，x j )＝<θ(x i )，θ(x j )>
wherein,,<θ(x i )，θ(x j )>represents the gradient direction square characteristic (x i ，x j ) Inner product calculation of nonlinear mapping, κ (x i ，x j ) For the gradient direction square feature (x i ，x j ) Is a non-linear mapping function of (2);
the constraint solution is as follows:
Wherein m is the gradient direction straightNumber of square features, alpha i ，α j Lagrangian number multiplied by a factor, y, solved for the constraint i ，y j Labels for the positive and negative samples, s.t, are constraints.
In the preferred embodiment of the present invention, the loss function is a least square method, and the loss function value is L (e):
wherein e is the error value between the training value of the black eye judgment model and the label set, k is the total number of the positive sample set and the negative sample set, y i For the tag set, y' i For the training value, the threshold is typically set to 0.01.
And step three, receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result.
The data mapping in the preferred embodiment of the invention adopts the nonlinear mapping method of the support vector machine.
Alternatively, in other embodiments, the black eye intelligent judging program may be further divided into one or more modules, where one or more modules are stored in the memory 11 and executed by one or more processors (the processor 12 in this embodiment) to complete the present invention, and the modules refer to a series of instruction segments of a computer program capable of performing a specific function, for describing the execution of the black eye intelligent judging program in the black eye intelligent judging device.
For example, referring to fig. 3, a schematic program module of a black eye intelligent judging program in an embodiment of the black eye intelligent judging apparatus according to the present invention is shown, where the black eye intelligent judging program may be divided into a data receiving module 10, a support vector machine training module 20, and a black eye judging module 30 by way of example:
the data receiving module 10 is configured to: the face image collection is received, the face image collection is divided into a positive sample collection and a negative sample collection through a label collection, and after preprocessing operations comprising graying, binarization and noise reduction are carried out on the positive sample collection and the negative sample collection, the positive sample collection, the negative sample collection and the label collection after preprocessing are input to a data processing layer.
The support vector machine training module 20 is configured to: receiving a positive sample set, a negative sample set and the tag set which are subjected to pretreatment, inputting the tag set into a black eye judgment model, carrying out direct gradient square operation on data of the positive sample set and the negative sample set, and then inputting the data into the black eye judgment model, wherein the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm, and quits training until a loss function value in the support vector machine algorithm is smaller than a threshold value.
The black eye judgment module 30 is configured to: receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result.
The functions or operation steps implemented when the program modules such as the data receiving module 10, the support vector machine training module 20, the black eye judgment module 30 and the like are executed are substantially the same as those of the foregoing embodiments, and will not be described herein.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a black eye intelligent judgment program is stored, where the black eye intelligent judgment program may be executed by one or more processors to implement the following operations:
receiving a face image set, wherein the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operation comprising graying, binarizing and noise reduction is carried out on the positive sample set and the negative sample set, and then the positive sample set, the negative sample set and the label set which are preprocessed are input to a data processing layer;
receiving a positive sample set, a negative sample set and the label set which are subjected to pretreatment, inputting the label set into a black eye judgment model, performing direct gradient square operation on data of the positive sample set and the negative sample set, and then inputting the data into the black eye judgment model, wherein the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm, and quits training until a loss function value in the support vector machine algorithm is smaller than a threshold value;
receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct gradient square operation on the mapped test set, inputting the direct gradient square operation to the black eye judgment model to judge whether the black eye exists or not, and outputting a result.
The specific embodiments of the computer readable storage medium of the present invention are substantially the same as the above-described embodiments of the black eye intelligent judging apparatus and method, and will not be described herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (6)
1. An intelligent judging method for black eyes is characterized by comprising the following steps:
the data receiving layer receives a face image set, the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operations comprising graying, binarizing and noise reduction are carried out on the positive sample set and the negative sample set, and the positive sample set, the negative sample set and the label set which are subjected to preprocessing are input to the data processing layer;
the data processing layer receives the positive sample set, the negative sample set and the label set which are subjected to pretreatment, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and then inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training;
receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct-square operation on the mapped test set in a direction gradient, inputting the direct-square operation to the black eye judgment model to judge whether a black eye exists or not, and outputting a result;
the method for determining the black eye comprises the steps of performing direct-square operation on data of the positive sample set and the negative sample set, and inputting the data into a black eye judgment model, wherein the method comprises the following steps: calculating gradient amplitude values and gradient direction values of all pixel points (x, y) of data in the face image set, and forming a gradient matrix by taking the gradient amplitude values as first components and the gradient direction values as second components; dividing the data in the gradient matrix into a plurality of small blocks, adding the gradient amplitude value and the gradient direction value of each small block, connecting the added values in series to form a gradient direction straight feature, and inputting the gradient direction straight feature into a black eye judgment model;
the support vector machine algorithm comprises nonlinear mapping and constraint solving;
the nonlinear mapping is:
κ(x i ,x j )＝<θ(x i ),θ(x j )>
wherein,,<θ(x i ),θ(x j )>represents the gradient direction square characteristic (x i ,x j ) Inner product calculation of nonlinear mapping, κ (x i ,x j ) For the gradient direction square feature (x i ,x j ) Is a non-linear mapping function of (2);
the constraint solution is as follows:
Wherein m is the number of the gradient direction rectangularity features, alpha i ，α j Lagrangian number multiplied by a factor, y, solved for the constraint i ，y j Labels that are positive and negative samples, s.t is a constraint.
2. The method of claim 1, wherein the data in the positive sample set is a face including black eye and the data in the negative sample set is a face not including black eye.
3. The method for intelligently judging the black eye according to claim 1 or 2, wherein the noise reduction processing adopts an adaptive image noise reduction filtering method:
g(x,y)＝η(x,y)+f(x,y)
wherein (x, y) represents coordinates of pixel points of an image, f (x, y) is output data obtained by performing noise reduction processing on the positive sample set and the negative sample set based on the adaptive image noise reduction filtering method, eta (x, y) is noise, g (x, y) is the positive sample set and the negative sample set,
for the total noise variance of the positive and negative sample sets, +.>
Is the pixel gray average value of the (x, y), is->
For the pixel gray variance of (x, y), L represents the current pixel point coordinates.
4. The device is characterized by comprising a memory and a processor, wherein the memory stores a black eye intelligent judging program which can run on the processor, and the black eye intelligent judging program realizes the following steps when being executed by the processor:
the data receiving layer receives a face image set, the face image set is divided into a positive sample set and a negative sample set through a label set, preprocessing operations comprising graying, binarizing and noise reduction are carried out on the positive sample set and the negative sample set, and the positive sample set, the negative sample set and the label set which are subjected to preprocessing are input to the data processing layer;
the data processing layer receives the positive sample set, the negative sample set and the label set which are subjected to pretreatment, inputs the label set into a black eye judgment model, carries out direct gradient square operation on the data of the positive sample set and the negative sample set, and then inputs the data into the black eye judgment model, and the black eye judgment model trains the negative sample set and the positive sample set based on a support vector machine algorithm until a loss function value in the support vector machine algorithm is smaller than a threshold value, and quits training;
receiving a test set of a user, mapping data in the test set to a high-dimensional space based on a nonlinear mapping method, performing direct-square operation on the mapped test set in a direction gradient, inputting the direct-square operation to the black eye judgment model to judge whether a black eye exists or not, and outputting a result;
the method for determining the black eye comprises the steps of performing direct-square operation on data of the positive sample set and the negative sample set, and inputting the data into a black eye judgment model, wherein the method comprises the following steps: calculating gradient amplitude values and gradient direction values of all pixel points (x, y) of data in the face image set, and forming a gradient matrix by taking the gradient amplitude values as first components and the gradient direction values as second components; dividing the data in the gradient matrix into a plurality of small blocks, adding the gradient amplitude value and the gradient direction value of each small block, connecting the added values in series to form a gradient direction straight feature, and inputting the gradient direction straight feature into a black eye judgment model;
the support vector machine algorithm comprises nonlinear mapping and constraint solving;
the nonlinear mapping is:
κ(x i ,x j )＝<θ(x i ),θ(x j )>
wherein,,<θ(x i ),θ(x j )>represents the gradient direction square characteristic (x i ,x j ) Inner product calculation of nonlinear mapping, κ (x i ,x j ) For the gradient direction square feature (x i ,x j ) Is a non-linear mapping function of (2);
the constraint solution is as follows:
Wherein m is the number of the gradient direction rectangularity features, alpha i ，α j Lagrangian number multiplied by a factor, y, solved for the constraint i ，y j Labels that are positive and negative samples, s.t is a constraint.
5. The intelligent judging apparatus of black eye according to claim 4, wherein the noise reduction process employs an adaptive image noise reduction filtering method:
g(x,y)＝η(x,y)+f(x,y)
wherein (x, y) represents coordinates of pixel points of an image, f (x, y) is output data obtained by performing noise reduction processing on the positive sample set and the negative sample set based on the adaptive image noise reduction filtering method, eta (x, y) is noise, g (x, y) is the positive sample set and the negative sample set,
for the total noise variance of the positive and negative sample sets, +.>
Is the pixel gray average value of the (x, y), is->
For the pixel gray variance of (x, y), L represents the current pixel point coordinates.
6. A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon a black eye intelligent judgment program executable by one or more processors to implement the steps of the black eye intelligent judgment method of any one of claims 1 to 3.
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