CN115862121B - Face quick matching method based on multimedia resource library - Google Patents

Face quick matching method based on multimedia resource library Download PDF

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CN115862121B
CN115862121B CN202310152207.XA CN202310152207A CN115862121B CN 115862121 B CN115862121 B CN 115862121B CN 202310152207 A CN202310152207 A CN 202310152207A CN 115862121 B CN115862121 B CN 115862121B
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CN115862121A (en
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李刚
董广智
徐国锋
林瑜
沙琨
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PLA Navy Submarine College
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Abstract

The invention relates to the technical field of image data processing, in particular to a face quick matching method based on a multimedia resource library, which comprises the following steps: intercepting a static image in a multimedia resource library, acquiring a corresponding gray level image, and acquiring a gray level histogram of the gray level image; marking a head region in the gray scale image as a region of interest; defining the adjustment coefficient of each gray level according to whether the gray level belongs to the concerned area, constructing a mapping function according to the adjustment coefficients of different gray levels and the gray level histogram, and obtaining non-key gray levels in the gray level histogram based on the mapping function; acquiring the interval width of each gray level in the concerned region according to the number of non-key gray levels and the gray level difference of the adjacent gray levels in the concerned region, and enhancing the gray level image based on the interval width to obtain an enhanced image; extracting facial features based on the enhanced image to perform face matching; the face matching method and the face matching device can improve the accuracy of face matching.

Description

Face quick matching method based on multimedia resource library
Technical Field
The invention relates to the technical field of image data processing, in particular to a face quick matching method based on a multimedia resource library.
Background
Face matching is a biological recognition technology for carrying out identity recognition based on facial feature information of people. The method has wide application in the fields of community access control, safe payment, employee attendance, intelligent ticket checking and the like, and the existing face matching mode has various modes, and can be basically divided into three steps: and recognizing a face image, selecting a face position by a frame, extracting features of the face, and finally carrying out face matching with a resource library, wherein if the matching result meets a certain threshold, the face matching is successful.
However, the existing face matching is real-time face matching, and the actual face recognition can also be applied to person tracking and recognition in monitoring videos or other multimedia images. The real-time face matching can flexibly change the distance and angle between the lens and the face and even the illumination environment, and the recorded multimedia image can only carry out face recognition on the basis of the determined shooting distance and definition, so that the recognition accuracy can be greatly reduced.
The multimedia image is usually pre-processed by the preprocessing module, but the image details are always lost by the actual conventional preprocessing algorithm, such as the most commonly used histogram equalization enhancement contrast, and although the fuzzy face is enhanced and can be identified from unrecognizable to identifiable, the enhanced face features may be incomplete and deformed, which may cause errors in face matching.
Disclosure of Invention
In order to solve the problem of error in face matching caused by poor histogram equalization enhanced image effect, the invention aims to provide a face quick matching method based on a multimedia resource library, and the adopted technical scheme is as follows:
the invention provides a face quick matching method based on a multimedia resource library, which comprises the following steps:
intercepting a static image in a multimedia resource library, acquiring a corresponding gray level image, and acquiring a gray level histogram of the gray level image;
marking a head region in the grayscale image as a region of interest; defining the adjustment coefficient of each gray level according to whether the gray level belongs to the concerned area, constructing a mapping function according to the adjustment coefficient of different gray levels and the gray level histogram, and obtaining non-key gray levels in the gray level histogram based on the mapping function;
acquiring the interval width of each gray level in the concerned region according to the number of the non-key gray levels and the gray level difference of the adjacent gray levels in the concerned region, and enhancing the gray level image based on the interval width to obtain an enhanced image;
and extracting facial features based on the enhanced image to perform face matching.
Preferably, the step of customizing the adjustment coefficient of each gray level according to whether the gray level belongs to the region of interest includes:
obtaining the occurrence probability corresponding to different gray levels based on the gray level histogram, taking any gray level as a first gray level, and if the first gray level is the gray level in the concerned region, the adjusting coefficient of the first gray level is the reciprocal of the product of the corresponding occurrence probability and a preset constant;
if the first gray level is not the gray level in the concerned area, acquiring a polynomial fitting curve of the gray histogram, and calculating the derivative of the polynomial fitting curve at the first gray level, if the derivative is smaller than zero, the adjustment coefficient of the first gray level is the ratio of the occurrence probability of the first gray level to the occurrence probability of the previous gray level; if the derivative is not less than zero, the adjustment coefficient of the first gray level is 1.
Preferably, the expression of the mapping function is:
Figure SMS_1
；
wherein ,
Figure SMS_3
indicate->
Figure SMS_6
A mapping function of the individual gray levels;
Figure SMS_8
Representing the>
Figure SMS_4
First ∈th gray level before>
Figure SMS_5
Probability of occurrence corresponding to the individual gray levels;
Figure SMS_9
Is a preset constant;
Figure SMS_10
Representing the>
Figure SMS_2
First ∈th gray level before>
Figure SMS_7
And the adjustment coefficients of the gray levels.
Preferably, the step of obtaining the non-emphasized gray level in the gray histogram based on the mapping function includes:
taking any gray level as a target gray level, rounding the mapping result of the target gray level obtained based on the mapping function to obtain a first result, and rounding the mapping result of the previous adjacent gray level of the target gray level to obtain a second result; if the first result is the same as the second result, the target gray level corresponding to the first result is a non-key gray level;
the gray level with zero occurrence probability in the gray histogram is a non-key gray level.
Preferably, the step of acquiring the interval width of each gray level in the region of interest based on the number of non-emphasized gray levels and the gray level difference of the adjacent gray levels in the region of interest includes:
counting the maximum gray level and the minimum gray level except the non-key gray level in the gray level histogram; constructing a calculation formula of interval width from the difference between the maximum gray level and the minimum gray level, the number of non-key gray levels and the gray level difference of adjacent gray levels in the region of interest, wherein the calculation formula of interval width is as follows:
Figure SMS_11
；
wherein ,
Figure SMS_13
representing the%>
Figure SMS_15
The width of the interval of the individual gray levels;
Figure SMS_19
Representing the number of all non-emphasized gray levels in the gray level histogram;
Figure SMS_14
Representing the%>
Figure SMS_16
Gray values of the individual gray levels;
Figure SMS_18
Representing the%>
Figure SMS_20
Gray values of the individual gray levels;
Figure SMS_12
Representing a maximum gray level in the gray histogram other than the non-emphasized gray level;
Figure SMS_17
Representing the minimum gray level in the gray histogram except for the non-emphasized gray level.
Preferably, the step of enhancing the gray image based on the interval width to obtain an enhanced image includes:
the interval width of each gray level in the concerned area is rounded downwards, the gray level is translated leftwards for the first gray level in the concerned area, and the translation scale is the same as the interval width of the first gray level after rounding downwards;
for any gray level except the first gray level in the concerned region, marking the gray level as a mark gray level, calculating the difference value between the mark gray level and the adjacent previous gray level in the concerned region, and if the difference value is larger than the interval width of the mark gray level after rounding downwards, translating the mark gray level leftwards, wherein the translation scale is the same as the interval width of the mark gray level after rounding downwards; if the difference value is not greater than the interval width of the mark gray level after downward rounding, shifting the mark gray level to the right, wherein the shifting scale is the same as the interval width of the mark gray level after downward rounding; and similarly, carrying out translational stretching on all gray levels of each gray level in the concerned region to obtain an enhanced image after the gray image is enhanced.
Preferably, the step of extracting facial features based on the enhanced image for face matching includes:
filtering and removing isolated points in the enhanced image, and acquiring characteristic points in the enhanced image after the isolated points are removed based on an LSD algorithm; extracting image feature points of an object to be identified, matching the image feature points with feature points in an enhanced image, and successfully matching faces when the number of the feature points reaches a preset proportion.
Preferably, the step of marking the head region in the grayscale image as the region of interest includes:
dividing the gray level image through a trained classifier to obtain a head region, and carrying out convolution processing on the head region by using a convolution check with a preset size to obtain a convolution value of each pixel point in the head region; calculating the mean square error of convolution values corresponding to all pixel points in the head region, and if the mean square error after normalization processing is not smaller than a preset threshold value, taking the head region as a concerned region;
the method for acquiring the convolution value of each pixel point comprises the following steps: and taking the pixel point as the center of a convolution kernel, wherein the gray average value of all the pixel points in the convolution kernel range is the convolution value of the pixel point.
The invention has the following beneficial effects: according to the embodiment of the invention, the enhancement effect of the histogram equalization enhancement image is improved by optimizing and modifying the existing histogram equalization, and the success rate and the accuracy of face matching can be effectively improved by carrying out subsequent face matching based on the enhancement image; when the histogram equalization is optimized and modified, the adjustment coefficients of different gray levels are customized according to whether the gray levels belong to the attention area or not by acquiring the attention area in the gray level image, so that the analysis of the image is more focused on the attention area, more effective information is reserved as much as possible, a mapping function is constructed through the adjustment coefficients corresponding to the different gray levels and the gray level histogram corresponding to the gray level image, gray levels in the gray level histogram are screened based on the mapping function, non-key gray levels in the gray level image are obtained, and the mapping result is interfered through the adjustment coefficients of the different gray levels, so that the loss of the effective information in the histogram equalization processing is avoided; the interval width of each gray level in the concerned region is further obtained based on the number of all non-key gray levels and the gray level difference of the adjacent gray levels in the concerned region, and then the gray level image is enhanced through the interval width to obtain an enhanced image.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a face quick matching method based on a multimedia resource library according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of a face rapid matching method based on a multimedia resource library according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the face quick matching method based on the multimedia resource library provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a face quick matching method based on a multimedia resource library according to an embodiment of the present invention is shown, and the method includes the following steps:
step S100, a static image is intercepted in a multimedia resource library, a corresponding gray level image is obtained, and a gray level histogram of the gray level image is obtained.
Face recognition can be applied to person tracking and recognition in monitoring videos or other multimedia images, the distance, angle and even illumination environment between a lens and a face can be flexibly changed when real-time face matching is performed, however, the recorded multimedia images can only be subjected to face recognition based on a fixed shooting distance and definition, recognition accuracy is poor, so that the multimedia images are usually enhanced, but when common histogram equalization is performed for image enhancement, image details are lost while contrast is enhanced, although the enhanced images can be recognized, enhancement effect is poor, the enhanced face features can be incomplete and deformed, and follow-up face matching is not facilitated.
In the embodiment, through adjusting the histogram equalization process, firstly, a static image of a related person is intercepted from a multimedia resource library, and for the convenience of analysis and processing, the static image is subjected to gray processing to obtain a corresponding gray image, so that the calculated amount is reduced, redundant color information is inhibited, and the subsequent extraction of face features is facilitated; and then acquiring a gray level histogram corresponding to each gray level image, wherein the abscissa of the gray level histogram is different gray levels, the ordinate is the occurrence probability of each gray level in the gray level image, the acquiring method of the gray level histogram is a known means, and in the embodiment, the detailed description is omitted, and subsequent analysis processing is performed based on the gray level histogram corresponding to each gray level image.
Step S200 of marking a face region in the grayscale image as a region of interest; and defining the adjustment coefficient of each gray level according to whether the gray level belongs to the concerned region, constructing a mapping function according to the adjustment coefficients of different gray levels and the gray level histogram, and obtaining the non-key gray level in the gray level histogram based on the mapping function.
In order to accurately identify the face information, preprocessing of the image is an indispensable ring, development of face recognition algorithms is enough perfected so far, various potential problems still exist due to different algorithm operation conditions, namely, clear face matching cases of the image, errors or recognition errors hardly exist, and face matching failure only occurs under the conditions of lower image quality and more complex environment, so that the preprocessing of the image needs to be emphasized in the process of optimizing the face matching to ensure the operation environment of the face matching algorithm.
The conventional image enhancement algorithm stretches or equalizes gray levels based on a gray level histogram to achieve the effect of enhancing image contrast, and histogram equalization eliminates less gray levels in an image based on a mapping function and accumulates the less gray levels on more gray levels, so that intervals among the gray levels become larger and contrast is enhanced; however, the gray level to be eliminated may be detailed information of the face of the person, and although the image becomes relatively clear after elimination, the face is distorted, so when the face matching is optimized, the gray image is initially divided to obtain the face region.
Dividing the gray level image through a trained classifier to obtain a head region, and carrying out convolution processing on the head region by utilizing a convolution check of a preset size to obtain a convolution value of each pixel point in the head region; calculating the mean square error of convolution values corresponding to all pixel points in the head region, and if the mean square error after normalization processing is not smaller than a preset threshold value, taking the head region as a concerned region; the method for acquiring the convolution value of each pixel point comprises the following steps: and taking the pixel point as the center of a convolution kernel, wherein the gray average value of all the pixel points in the convolution kernel range is the convolution value of the pixel point.
Specifically, a classifier is set for dividing a face region in a gray level image, wherein the classifier consists of a character contour recognition network after training and a face recognition module; the figure outline recognition network is trained by using Human Pose Evaluator human outline recognition image data and is used for recognizing the figure outline in the image, 6 parts of the figure outline are represented by the head, the trunk, the left and right big arms and the left and right small arms, and each part is represented by a line segment; the human body contour recognition network is a CNN neural network actually, the loss function adopts a cross entropy loss function, a large number of character images marked according to a Human Pose Evaluator mode are input into the neural network for training, the pixel points of the character contour are marked as 1 in the training process, the pixel points of the non-character contour are marked as 0, and the detailed training process is a known technology and is not repeated in detail; whereby the contour of the person in each gray scale image can be obtained.
Then, a head area is framed in the character outline of the gray image, the head area is obtained when the character outline is obtained, the character outline information is different due to different character facing angles, for example, the front face and the back face of the character are not provided with face information, therefore, the embodiment of the invention adopts a convolution check with the size of 5*5 to carry out traversal convolution on the framed head area, no element value exists in convolution kernel, and the convolution process is to sum and average gray values of pixel points in the 5*5 range of each pixel point to be used as convolution values of the pixel points at the central position, so that convolution values corresponding to all the pixel points in the head area are obtained; and then calculating the mean square error of the convolution values corresponding to all the pixel points in the head region, and carrying out normalization processing on the mean square error to obtain the convolution mean square error corresponding to the head region.
Since no face information exists on the back of the head region, the gray information of the back region is single, whether the head region is a face region is judged by comparing the convolution mean square error corresponding to the head region with an empirical threshold value, the empirical threshold value is 0.3 in the embodiment, an implementer can adjust the method in other embodiments, and when the convolution mean square error of the head region is smaller than 0.3, the head region is judged to have no face information; when the convolution mean square error of the head region is not less than 0.3, it is determined that the head region has face information, and the head region having face information is referred to as a region of interest.
The step of determining face information by the convolution value is only to acquire the region of interest in which the face information of the person exists, and it is not necessary to determine detailed face information, so that the gray-scale image may be roughly divided as long as the gray-scale image is not distorted to such an extent that it is completely unrecognizable.
Further, an adjustment coefficient is set to control a histogram equalization mapping function to keep and eliminate gray levels in a gray level image, the gray level contained in a concerned region is marked as a concerned gray level because the gray level histogram does not show position information, the concerned gray level is marked in the gray level histogram, the essence of the histogram equalization enhancement image quality is to enlarge the difference between the gray levels, and the image directly subjected to the histogram equalization can lose detail information, so that the embodiment of the invention ensures that the gray level difference is as large as possible on the basis that the concerned gray level can be kept, so as to control the histogram mapping function to eliminate and keep the gray level, and the mapping function is specifically as follows:
Figure SMS_21
；/>
wherein ,
Figure SMS_22
representing a mapping function;
Figure SMS_23
Indicate->
Figure SMS_24
The probability of occurrence of a gray level correspondence, i.e.>
Figure SMS_25
Probability of occurrence of individual gray levels in a gray image;
Figure SMS_26
In the embodiment of the invention, the value is the number of different gray levels, namely the number of gray levels appearing in the gray image, and the maximum value is 256;
Figure SMS_27
Indicate->
Figure SMS_28
And the adjustment coefficients of the gray levels.
Mapping function
Figure SMS_30
The essence of (a) is an increasing function, accumulating one gray level at a time corresponding +.>
Figure SMS_33
The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining mapping results corresponding to different gray levels in the gray level image, when +.>
Figure SMS_34
Rounding and rounding of the mapping result of the individual gray levels with +.>
Figure SMS_31
When the rounding of the mapping result of the gray levels is not equal, then +.>
Figure SMS_32
The individual gray levels are reserved gray levels; conversely, when->
Figure SMS_35
Rounding and rounding of the mapping result of the individual gray levels with +.>
Figure SMS_36
When the rounding of the mapping result of the gray levels is equal, the +.>
Figure SMS_29
The gray level is eliminated, that is, partial gray level in the gray level histogram is reserved through the mapping result, and partial gray level in the gray level histogram is subjected to sacrificial treatmentAnd (3) drawing gaps are reserved in the histogram, so that the subsequent operation of gray scale drawing is facilitated, and the eliminated gray scale is marked as a non-key gray scale.
Obtaining the occurrence probability corresponding to different gray levels based on the gray level histogram, taking any gray level as a first gray level, and if the first gray level is the gray level in the concerned region, taking the adjusting coefficient of the first gray level as the reciprocal of the product of the corresponding occurrence probability and a preset constant; if the first gray level is not the gray level in the concerned area, acquiring a polynomial fitting curve of a gray histogram, and calculating the derivative of the polynomial fitting curve at the first gray level, wherein if the derivative is smaller than zero, the adjustment coefficient of the first gray level is the ratio of the occurrence probability of the first gray level to the occurrence probability of the previous gray level; if the derivative is not less than zero, the adjustment coefficient of the first gray level is 1.
The adjustment coefficients are specifically:
Figure SMS_37
；
wherein ,
Figure SMS_40
indicate->
Figure SMS_41
Gray values of the individual gray levels;
Figure SMS_44
Representing the%>
Figure SMS_39
Gray values of the gray levels of interest;
Figure SMS_43
Indicate->
Figure SMS_46
Probability of occurrence of individual gray levels;
Figure SMS_47
Representation ofFirst->
Figure SMS_38
Probability of occurrence corresponding to the individual gray levels;
Figure SMS_42
A polynomial fitting curve corresponding to the gray level histogram is represented at +.>
Figure SMS_45
The result of the derivative at the individual gray levels.
When (when)
Figure SMS_50
When, i.e. when +.>
Figure SMS_55
Gray values of the gray levels and +.>
Figure SMS_62
When the gray values of the gray levels of interest are equal, the first +.>
Figure SMS_49
The gray level is concerned, and the gray level should be reserved in a way that the gray level is necessarily amplified, but the mapping result cannot be directly determined to be amplified by more than two times to realize the reservation, so the most stable way is selected to be the (th)>
Figure SMS_54
The adjustment coefficient corresponding to the gray level is set to +.>
Figure SMS_57
Making its mapping result equal to 1 in any case; when->
Figure SMS_59
and
Figure SMS_51
When indicate->
Figure SMS_53
The probability of occurrence of each gray level in the gray level histogram is smaller than that of the adjacent previous gray level, the ordinate value of the gray level histogram is the probability of occurrence of different gray levels, and the +.>
Figure SMS_56
The gray level is not the focus gray level, so at this point +.>
Figure SMS_60
The gray level is not necessarily the gray level to be preserved and the probability of the distribution of pixels is smaller for the neighboring previous gray level, then +.>
Figure SMS_63
The gray level is a gray level that can be considered for sacrifice in order to facilitate the subsequent stretching of the image contrast, thus will be +.>
Figure SMS_66
The adjustment coefficient corresponding to the gray level is set to +.>
Figure SMS_68
Since the precondition at this time is a polynomial fitting curve corresponding to the gray level histogram
Figure SMS_71
In->
Figure SMS_65
The result of the derivation at the individual gray levels is negative, i.e. +.>
Figure SMS_67
Thus when->
Figure SMS_69
When the probability of the distribution of the gray levels is too small compared to its neighboring preceding gray level, the corresponding adjustment coefficient +.>
Figure SMS_70
Will take a small value, the +.>
Figure SMS_48
The number of gray levels is considered to be gray levels that can be eliminated to achieve gray stretching and do not affect the region of interest information; when adjusting the coefficient->
Figure SMS_52
When the mapping result is close to 1, the mapping result does not have large change, and the originally reserved gray level can be reserved. It should be noted that when->
Figure SMS_58
and
Figure SMS_61
At the time->
Figure SMS_64
The adjustment coefficient of each gray level is 1.
As an example, assume the first derived based on a mapping function
Figure SMS_72
The mapping result for the respective gray level is 50.2, i.e. is added to the +.>
Figure SMS_77
The accumulated result of the gray levels is 50.2, and the value after rounding is 50; if based on mapping function get +>
Figure SMS_79
The mapping result corresponding to the gray level is 49.8, then +.>
Figure SMS_74
The value after rounding of the mapping result corresponding to the gray level is also 50, the +.>
Figure SMS_81
Gray level and->
Figure SMS_82
The mapping results of the individual gray levels are equal after roundingFirst->
Figure SMS_83
The individual gray levels should be eliminated; but if at this time->
Figure SMS_73
The gray level is the gray level of interest, then at +.>
Figure SMS_76
The mapping result of the individual grey levels is given +.>
Figure SMS_78
The mapping result for the individual gray levels is 49.8+1=50.8, the rounding result at this time is 51, and +.>
Figure SMS_80
The rounding mapping result 50 of the gray levels is different, so +.>
Figure SMS_75
The individual gray levels are preserved.
Step S300, the interval width of each gray level in the concerned region is obtained according to the number of non-key gray levels and the gray level difference of the adjacent gray levels in the concerned region, and the gray image is enhanced based on the interval width to obtain an enhanced image.
Acquiring gray levels to be eliminated in the gray level histogram in the step S200, counting the number of all non-key gray levels in the gray level histogram, wherein the non-key gray levels in the embodiment of the invention comprise gray levels which have no occurrence probability in the gray level histogram except the gray levels to be eliminated, namely, the gray level with the value of 0 on the ordinate in the gray level histogram is also the non-key gray level, and the interval width is allocated to each concerned gray level based on the number of the non-key gray levels and the gray level difference between adjacent gray levels in the concerned region, and counting the maximum gray level and the minimum gray level except the non-key gray level in the gray level histogram; constructing a calculation formula of interval width from the difference between the maximum gray level and the minimum gray level, the number of non-key gray levels and the gray level difference of adjacent gray levels in the concerned region, wherein the specific calculation of the interval width is as follows:
Figure SMS_84
；
wherein ,
Figure SMS_86
representing the%>
Figure SMS_88
The width of the interval of the gray levels, i.e. +.>
Figure SMS_90
The interval width of the gray level of interest;
Figure SMS_87
Representing the number of all non-emphasized gray levels in the gray level histogram;
Figure SMS_89
Representing the%>
Figure SMS_91
Gray values of the individual gray levels;
Figure SMS_93
Representing the%>
Figure SMS_85
Gray values of the individual gray levels;
Figure SMS_92
Representing a maximum gray level in the gray histogram other than the non-emphasized gray level;
Figure SMS_94
Representing the minimum gray level in the gray histogram except for the non-emphasized gray level.
Figure SMS_97
Representing the%>
Figure SMS_100
The (th) of the gray levels adjacent thereto>
Figure SMS_102
The gray level difference between the gray levels, the arrangement of the gray levels in the gray level histogram is sequentially increased, thus +.>
Figure SMS_95
The gray level must be greater than the first
Figure SMS_99
Gray levels;
Figure SMS_103
The difference between the maximum gray level and the minimum gray level except for the non-key gray level on the gray level histogram, namely the maximum interval of the gray levels on the gray level histogram is used for normalizing the gray level difference between the adjacent gray levels, wherein the purpose of normalizing by the maximum interval is to consider that limitation exists when gray stretching is carried out on a gray level image;
Figure SMS_105
The smaller the value of (2), the smaller the interval between the adjacent gray levels, and the more the interval needs to be stretched;
Figure SMS_96
The larger the value of (2), the less stretch is required for some interval, thus +.>
Figure SMS_98
As the stretching degree, the number of non-emphasized gray levels is the total amount which can be stretched and distributed, and the product of the stretching degree and the number of non-emphasized gray levels is +.>
Figure SMS_101
To obtain a specific stretching amount, i.e. < ->
Figure SMS_104
The width of the interval of the individual gray levels.
The interval width of each gray level in the concerned area is rounded downwards, the gray level is translated leftwards for the first gray level in the concerned area, and the translation scale is the same as the interval width of the first gray level after rounding downwards; for any gray level except the first gray level in the concerned area, marking the gray level as a mark gray level, calculating the difference value between the mark gray level and the adjacent previous gray level in the concerned area, and if the difference value is larger than the interval width of the mark gray level after downward rounding, translating the mark gray level to the left, wherein the translation scale is the same as the interval width of the mark gray level after downward rounding; if the difference value is not greater than the interval width of the mark gray level after downward rounding, translating the mark gray level to the right, wherein the translation scale is the same as the interval width of the mark gray level after downward rounding; and carrying out translation stretching on all gray levels in the concerned region to obtain an enhanced image after the gray level image is enhanced.
Specifically, the interval width of each concerned gray level is rounded downwards, so that the rounded interval width corresponding to each concerned gray level in the concerned region is obtained; if the gray level of one gray level image is distributed between 0 and 150, the space where the gray level can be horizontally stretched to the right has 155 gray levels, and the stretched gray level image increases the contrast, but the original gray level image has serious distortion and overexposure; accordingly, the excessive shift of the gray level to the left causes serious distortion and excessive darkness, so that the stretching and shifting are performed in the original gray distribution interval of the gray image in the embodiment.
Sequentially from the first gray level in the region of interest, when
Figure SMS_123
When (I)>
Figure SMS_126
The value is +.>
Figure SMS_127
Obtaining the corresponding interval width +.>
Figure SMS_107
Will->
Figure SMS_113
The gray level is shifted left +.>
Figure SMS_117
Gray levels; when->
Figure SMS_119
When the user is required to judge the->
Figure SMS_109
Gray level and->
Figure SMS_112
Whether the interval between the individual gray levels is larger than the corresponding interval width +.>
Figure SMS_115
If->
Figure SMS_121
Gray level and->
Figure SMS_116
The interval between the grey levels is larger than +.>
Figure SMS_120
The interval width corresponding to the individual gray levels +.>
Figure SMS_125
Will->
Figure SMS_128
The gray level is shifted left +.>
Figure SMS_122
Gray levels; if%>
Figure SMS_124
Gray level and->
Figure SMS_129
The interval between the gray levels is not more than +.>
Figure SMS_130
The interval width corresponding to the individual gray levels +.>
Figure SMS_106
Will->
Figure SMS_110
Shift right gray level +.>
Figure SMS_114
Gray levels; when->
Figure SMS_118
The interval width corresponding to the individual gray levels +.>
Figure SMS_108
When equal to 0, indicate +.>
Figure SMS_111
The contrast at each gray level is enough, stretching is not needed, and the like, each gray level in the concerned region is analyzed and processed until stretching is completed on all gray levels in the concerned region, namely, the gray level image is stretched and enhanced, and the image after the stretching and enhancing is recorded as an enhanced image.
The gap width in the stretching operation is herein
Figure SMS_131
The values are obtained after the downward rounding treatment.
Step S400, face matching is performed based on the face features extracted from the enhanced image.
The enhanced image subjected to histogram equalization and stretching retains all the gray levels of interest, and the gaps among all the gray levels of interest in the interest area are adaptively stretched within an allowable range, so that the purpose of enhancing the contrast of the interest area is achieved; however, it is inevitable that isolated points exist in the enhanced image of the gray level image, and the original isolated points are more prominent after stretching enhancement, so that all the isolated points in the enhanced image are processed first; filtering and removing isolated points in the enhanced image, and acquiring characteristic points in the enhanced image after removing the isolated points based on an LSD algorithm; and extracting image feature points of the object to be identified, matching the image feature points with feature points in the enhanced image, and successfully matching the human face when the number of the feature points is up to a preset proportion.
Specifically, in the embodiment of the present invention, the filter of 3*3 is used to verify the isolated point, that is, the gray values of all the pixel points in the range of the isolated point 3*3 are summed and averaged, and the average value is given to the isolated point to update the gray value of the isolated point; the method for judging the isolated point comprises the following steps: for any pixel point, if the eight neighborhood pixel points of the pixel point do not have the neighborhood pixel points with the same gray value as the pixel point, the pixel point is judged to be an isolated point.
Further, face matching is performed based on the enhanced image after the isolated points are eliminated; for the attention area in the enhanced image, the embodiment of the invention utilizes a Canny operator to detect the facial outline and the five sense organs edge, then utilizes an LSD algorithm to extract at least 68 feature points from the face, the nature of the LSD algorithm is to detect the local outline in the image, the feature points are the end points on two sides of a stable outline, the feature points are not affected by the small expression change of the face or the orientation angle of the person, at least 68 basic feature points are obtained by referring to the existing face detection Dlib library, and the application range and the reliability are higher; when the faces are matched, facial image feature points of objects to be identified are obtained in advance, then the feature points of the faces are matched in the interesting areas, namely the feature points of the faces, in each multimedia image of the multimedia resource library in a mode of image pyramid, translation and rotation, and when the number of the feature point matching reaches a preset proportion, the faces are successfully matched; in this embodiment, the preset ratio is set to 95%, and in other embodiments, the practitioner can adjust the ratio by himself; the Canny operator and the LSD algorithm are all known means and are not described in detail.
In summary, in the embodiment of the present invention, the gray histogram of the gray image is obtained by capturing the still image and obtaining the corresponding gray image in the multimedia resource library; marking a head region in the gray scale image as a region of interest; defining the adjustment coefficient of each gray level according to whether the gray level belongs to the concerned area, constructing a mapping function according to the adjustment coefficients of different gray levels and the gray level histogram, and obtaining non-key gray levels in the gray level histogram based on the mapping function; acquiring the interval width of each gray level in the concerned region according to the number of non-key gray levels and the gray level difference of the adjacent gray levels in the concerned region, and enhancing the gray level image based on the interval width to obtain an enhanced image; extracting facial features based on the enhanced image to perform face matching; the reliability and the accuracy of face matching are effectively improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.

Claims (5)

1. A face quick matching method based on a multimedia resource library is characterized by comprising the following steps:
intercepting a static image in a multimedia resource library, acquiring a corresponding gray level image, and acquiring a gray level histogram of the gray level image;
marking a head region in the grayscale image as a region of interest; defining the adjustment coefficient of each gray level according to whether the gray level belongs to the gray level in the concerned area, constructing a mapping function according to the adjustment coefficients of different gray levels and the gray level histogram, and obtaining non-key gray levels in the gray level histogram based on the mapping function;
acquiring the interval width of each gray level in the concerned region according to the number of the non-key gray levels and the gray level difference of the adjacent gray levels in the concerned region, and enhancing the gray level image based on the interval width to obtain an enhanced image;
extracting facial features based on the enhanced image to perform face matching;
the step of obtaining the non-emphasized gray level in the gray level histogram based on the mapping function includes:
taking any gray level as a target gray level, rounding the mapping result of the target gray level obtained based on the mapping function to obtain a first result, and rounding the mapping result of the previous adjacent gray level of the target gray level to obtain a second result; if the first result is the same as the second result, the target gray level corresponding to the first result is a non-key gray level;
the gray level with zero occurrence probability in the gray histogram is a non-key gray level;
the step of acquiring a gap width of each gray level in the region of interest based on the number of non-emphasized gray levels and the gray level difference of adjacent gray levels in the region of interest includes:
counting the maximum gray level and the minimum gray level except the non-key gray level in the gray level histogram; constructing a calculation formula of interval width from the difference between the maximum gray level and the minimum gray level, the number of non-key gray levels and the gray level difference of adjacent gray levels in the region of interest, wherein the calculation formula of interval width is as follows:
Figure QLYQS_1
，
wherein ,
Figure QLYQS_4
representing the%>
Figure QLYQS_7
The width of the interval of the individual gray levels;
Figure QLYQS_8
Representing the number of all non-emphasized gray levels in the gray level histogram;
Figure QLYQS_3
Representing the%>
Figure QLYQS_5
Gray values of the individual gray levels;
Figure QLYQS_9
Representing the first in the region of interest
Figure QLYQS_10
Gray values of the individual gray levels;
Figure QLYQS_2
Representing a maximum gray level in the gray histogram other than the non-emphasized gray level;
Figure QLYQS_6
Representing a minimum gray level in the gray histogram other than the non-emphasized gray level;
the step of enhancing the gray image based on the interval width to obtain an enhanced image comprises the following steps:
the interval width of each gray level in the concerned area is rounded downwards, the gray level is translated leftwards for the first gray level in the concerned area, and the translation scale is the same as the interval width of the first gray level after rounding downwards;
for any gray level except the first gray level in the concerned region, marking the gray level as a mark gray level, calculating the difference value between the mark gray level and the adjacent previous gray level in the concerned region, and if the difference value is larger than the interval width of the mark gray level after rounding downwards, translating the mark gray level leftwards, wherein the translation scale is the same as the interval width of the mark gray level after rounding downwards; if the difference value is not greater than the interval width of the mark gray level after downward rounding, shifting the mark gray level to the right, wherein the shifting scale is the same as the interval width of the mark gray level after downward rounding; and similarly, carrying out translational stretching on each gray level in the concerned region to obtain an enhanced image after the gray level image is enhanced.
2. The method for fast face matching based on a multimedia resource library according to claim 1, wherein the step of customizing the adjustment coefficient of each gray level according to whether the gray level belongs to the gray level in the attention area comprises:
obtaining the occurrence probability corresponding to different gray levels based on the gray level histogram, taking any gray level as a first gray level, and if the first gray level is the gray level in the concerned region, the adjusting coefficient of the first gray level is the reciprocal of the product of the corresponding occurrence probability and a preset constant;
if the first gray level is not the gray level in the concerned area, acquiring a polynomial fitting curve of the gray histogram, and calculating the derivative of the polynomial fitting curve at the first gray level, if the derivative is smaller than zero, the adjustment coefficient of the first gray level is the ratio of the occurrence probability of the first gray level to the occurrence probability of the previous gray level; if the derivative is not less than zero, the adjustment coefficient of the first gray level is 1.
3. The method for fast matching a face based on a multimedia resource library according to claim 2, wherein the expression of the mapping function is:
Figure QLYQS_11
，
wherein ,
Figure QLYQS_14
indicate->
Figure QLYQS_16
A mapping function of the individual gray levels;
Figure QLYQS_19
Representing the>
Figure QLYQS_13
First ∈th gray level before>
Figure QLYQS_17
Probability of occurrence corresponding to the individual gray levels;
Figure QLYQS_18
Is a preset constant;
Figure QLYQS_20
Representing the>
Figure QLYQS_12
First ∈th gray level before>
Figure QLYQS_15
And the adjustment coefficients of the gray levels.
4. The method for fast face matching based on multimedia resources according to claim 1, wherein the step of extracting facial features based on the enhanced image for face matching comprises:
filtering and removing isolated points in the enhanced image, obtaining feature points in the enhanced image after the isolated points are removed, extracting image feature points of an object to be identified, matching the image feature points of the object to be identified with the feature points in the enhanced image, and when the number of the feature point matching reaches a preset proportion, successfully matching the faces.
5. The method for fast face matching based on a multimedia resource library according to claim 1, wherein said step of marking a head region in said gray scale image as a region of interest comprises:
dividing the gray level image through a trained classifier to obtain a head region, and carrying out convolution processing on the head region by using a convolution check with a preset size to obtain a convolution value of each pixel point in the head region; calculating the mean square error of convolution values corresponding to all pixel points in the head region, and if the mean square error after normalization processing is not smaller than a preset threshold value, taking the head region as a concerned region;
the method for acquiring the convolution value of each pixel point comprises the following steps: and taking the pixel point as the center of a convolution kernel, wherein the gray average value of all the pixel points in the convolution kernel range is the convolution value of the pixel point.
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