CN113569872B - Multi-resolution shoe-wearing footprint sequence identification method based on pressure significance - Google Patents
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Abstract
The invention provides a multi-resolution shoe-wearing footprint sequence recognition method based on pressure significance, which relates to the technical field of footprint recognition and comprises an offline training process and an online recognition process; the offline training process at least comprises the following steps: acquiring a footprint pressure energy graph group; calculating local gray scale statistical characteristics of the footprint pressure energy image group; screening the pressure significance region of the footprint pressure energy map set; the online identification process at least comprises the following steps: acquiring a footprint pressure energy graph group; repairing the pressure significance region of the footprint pressure energy map set; constructing a multi-resolution footprint energy graph group; and calculating the matching score of the multi-resolution footprint energy image group to be identified and the multi-resolution footprint energy image group feature library, thereby obtaining the identification result of the multi-resolution shoe-wearing footprint sequence. According to the invention, the local information difference of the footprint image is considered, the image is divided into local area blocks for extracting gray statistical characteristics, and more accurate and stable characteristics are obtained.
Description
Technical Field
The invention relates to the technical field of footprint identification, in particular to a multi-resolution shoe-wearing footprint sequence identification method based on pressure significance.
Background
Biometric identification based on sequence footprint can be classified into on-line footprint sequence identification and off-line footprint sequence identification. The on-line footprint sequence identification method comprises the following steps: (1) And extracting the pressure peak value, and identifying the time for obtaining the pressure peak value and the pressure change curve as characteristics. (2) And acquiring images at all moments in the one-step footprint forming process and accumulated images formed at all moments, and combining a depth residual error network and an SVM to identify. However, online footprint sequence identification is mainly aimed at barefoot footprints, acquisition is difficult, and application scenes are few, which is not in line with practical application.
The off-line shoe-wearing footprint identification method comprises the following steps: (1) extracting footprint stride features: and (5) quantitatively analyzing the step length, the step width, the step angle and the like. (2) And constructing a footprint pressure energy graph group, and calculating a similarity matching score for identification. However, most of shoe wearing sequence footprint identification is based on stride characteristics, and the quantitative use of stride characteristics has instability and cannot be accurately identified; the recognition method based on the footprint pressure energy image group is greatly affected by the sole pattern, and the difference of the information quantity contained in different areas in the shoe print image is not considered. In view of the foregoing, a footprint sequence recognition method that takes into account the local information differences of footprint images is to be invented.
Disclosure of Invention
The invention provides a multi-resolution shoe-wearing footprint sequence identification method based on pressure significance, which solves the problem that the existing footprint sequence identification method does not consider the local information difference of footprint images.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a multi-resolution shoe-wearing footprint sequence identification method based on pressure significance comprises an offline training process and an online identification process;
the offline training process at least comprises the following steps:
acquiring a footprint pressure energy graph group;
calculating local gray scale statistical characteristics of the footprint pressure energy image group;
screening the pressure significance region of the footprint pressure energy map set;
repairing the pressure significance region of the footprint pressure energy map set;
extracting a pressure significance region of the footprint pressure energy map set;
constructing a multi-resolution footprint energy graph group;
constructing a multi-resolution footprint energy graph group feature library;
the online identification process at least comprises the following steps:
acquiring a footprint pressure energy graph group;
calculating local gray scale statistical characteristics of the footprint pressure energy image group;
screening the pressure significance region of the footprint pressure energy map set;
repairing the pressure significance region of the footprint pressure energy map set;
extracting a pressure significance region of the footprint pressure energy map set;
constructing a multi-resolution footprint energy graph group;
and calculating the matching score of the multi-resolution footprint energy image group to be identified and the multi-resolution footprint energy image group feature library, thereby obtaining the identification result of the multi-resolution shoe-wearing footprint sequence.
Preferably, the constructing of the footprint pressure energy map set includes:
denoising the original footprint sequence image, and performing segmentation, clipping and weighted superposition on the horizontal projection of the sequence image to obtain a left gait energy image group I 1 Right gait energy diagram group I 2 Left stride energy pattern group I 3 Right stride energy pattern group I 4 Left step width energy diagram group I 5 And right step width energy map group I 6 The footprint pressure energy diagram group is I S ，I S ＝{I k ,k＝1,2,3,4,5,6}。
Preferably, the calculating the local gray scale statistics of the footprint pressure energy map set comprises: select I S The single image in (a) is marked as I, the I is divided into non-overlapped local area blocks with the size of t multiplied by t, m multiplied by n are used for calculating local information entropy H in each area block,
wherein p (v) represents the probability that the gray value of a pixel in the region block is v,
generating a local entropy matrix I of I H ，
Calculating the gray average value in each region block to generate a local average matrix I of I M ，
Preferably, the screening of the pressure significance region comprises:
calculation I H Mean of non-0 elements;
calculation I M Mean of non-0 elements;
calculating a significance entropy binary matrix BOH;
matrix I M And performing AND operation on the sum matrix BOH according to elements to generate a matrix I MH ；
Pair I MH Gaussian blur is performed to obtain a matrix I MHG ；
Calculating a matrix BOMHG;
and performing contour detection on the BOMHG to generate a binary matrix BMHG of the pressure significance region.
Preferably, the patching of the pressure saliency area of the footprint pressure energy map set comprises:
amplifying the BMHG to be consistent with the original image I in size by adopting a nearest neighbor interpolation method to obtain the IBMHG;
extracting a pressure energy diagram significance region IP;
generating a mask matrix m according to the IP;
repairing the IP by adopting an image repairing method, wherein the repairing position is a point marked as 1 in M, and the image repairing is carried out by adopting a rapid travelling method in the implementation;
updating the corresponding position value in the I through the repaired matrix IP;
the pressure saliency region of the footprint pressure energy map set is extracted.
Preferably, the constructing the multi-resolution footprint energy map set includes:
pair I S The partial gray statistical features and the pressure significance areas of the footprint pressure energy image group are calculated and screened for each image to obtain a processed imageAn image;
and carrying out multi-scale Gaussian blur on the processed image to generate a multi-resolution footprint pressure energy image group.
The invention has the beneficial effects that:
according to the invention, the local information difference of the footprint image is considered, the image is divided into local area blocks for extracting gray statistical characteristics, and more accurate and stable characteristics are obtained;
according to the invention, the pressure significance region is extracted through screening of gray statistical features, and higher weight is given to the region, so that the distinguishing property among different people can be increased;
according to the invention, the image repairing is carried out on the image pressure significance region, then the multi-resolution footprint energy image group is constructed, the footprint pressure energy image group is expressed in various forms, the influence of sole patterns can be reduced, and the extracted information is more complete.
Drawings
For a clearer description of an embodiment of the invention or of the prior art, the drawings that are used in the description of the embodiment or of the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a partial mean view of a right gait energy diagram at a first resolution in an embodiment of the invention.
Fig. 3 is a partial entropy diagram of a right gait energy diagram at a first resolution in an embodiment of the invention.
Fig. 4 is a graph of the mean pressure significance of the right gait energy graph at a first resolution in an embodiment of the invention.
Fig. 5 is a pressure significance entropy diagram of a right gait energy diagram at a first resolution in an embodiment of the invention.
FIG. 6 is a partial mean view of a right gait energy pattern at a second resolution in an embodiment of the invention.
Fig. 7 is a partial entropy diagram of a right gait energy diagram at a second resolution in an embodiment of the invention.
FIG. 8 is a graph of the mean pressure significance of the right gait energy graph at a second resolution in an embodiment of the invention.
Fig. 9 is a pressure significance entropy diagram of a right gait energy diagram at a second resolution in an embodiment of the invention.
FIG. 10 is a partial mean view of a right gait energy graph at a third resolution in an embodiment of the invention.
Fig. 11 is a partial entropy diagram of a right gait energy diagram at a third resolution in an embodiment of the invention.
FIG. 12 is a graph of the mean pressure significance of the right gait energy graph at a third resolution in an embodiment of the invention.
Fig. 13 is a pressure significance entropy diagram of a right gait energy diagram at a third resolution in an embodiment of the invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. Meanwhile, it should be clear that the dimensions of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In the description of the present invention, it should be understood that the azimuth or positional relationships indicated by the azimuth terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal", and "top, bottom", etc., are generally based on the azimuth or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify the description, and these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be constructed and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present invention: the orientation word "inner and outer" refers to inner and outer relative to the contour of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present invention.
Examples
The invention provides a technical scheme that: a multi-resolution shoe-wearing footprint sequence identification method based on pressure significance is shown in a flow chart in fig. 1, and comprises an offline training process and an online identification process;
the offline training process at least comprises the following steps:
acquiring a footprint pressure energy graph group;
calculating local gray scale statistical characteristics of the footprint pressure energy image group;
screening the pressure significance region of the footprint pressure energy map set;
repairing the pressure significance region of the footprint pressure energy map set;
extracting a pressure significance region of the footprint pressure energy map set;
constructing a multi-resolution footprint energy map set, as shown in FIGS. 2-13;
constructing a multi-resolution footprint energy graph group feature library;
the online identification process at least comprises the following steps:
acquiring a footprint pressure energy graph group;
calculating local gray scale statistical characteristics of the footprint pressure energy image group;
screening the pressure significance region of the footprint pressure energy map set;
repairing the pressure significance region of the footprint pressure energy map set;
extracting a pressure significance region of the footprint pressure energy map set;
constructing a multi-resolution footprint energy graph group;
and calculating the matching score of the multi-resolution footprint energy image group to be identified and the multi-resolution footprint energy image group feature library, thereby obtaining the identification result of the multi-resolution shoe-wearing footprint sequence.
1. Footprint pressure energy diagram set construction
Denoising the original footprint sequence image, and performing segmentation, clipping and weighted superposition on the horizontal projection of the sequence image to obtain a left gait energy image group I 1 Right gait energy diagram group I 2 Left stride energy pattern group I 3 Right stride energy pattern group I 4 Left step width energy diagram group I 5 Right step width energy map group I 6 . I.e. footprint pressure energy map set I S ＝{I k ,k＝1,2,3,4,5,6}。
2. Pressure salient region extraction and repair
2.1 computing local Gray statistical characteristics
Select I S The single image in (a) is marked as I, the I is divided into non-overlapped local area blocks with the size of t multiplied by t, m multiplied by n are used for calculating the local information entropy in each area block
p (v) represents the probability of the gray value v of the pixel in the regional block, and generates a local entropy matrix of ICalculate eachGray level mean value in region block, generating local mean matrix +.>
2.2 pressure significance region screening
Calculation I H Mean value of non-0 element in Chinesenum H Is I H The number of non-0 elements in the list. Calculation I M Mean +.0 for middle element>num M Is I M The number of non-0 elements in the list.
A significance entropy binary matrix BOH is calculated,
matrix I M And performing AND operation on the sum matrix BOH according to elements to generate a matrix I MH I.e. I MH ＝I M &BOH，&Are by element and operation. Pair I MH Performing Gaussian blur, wherein the standard deviation of the Gaussian function is 2 sigma 0 In practice sigma 0 =11, resulting in matrix I MHG ，Calculating the matrix BOMHG,>and (3) performing contour detection on the BOMHG, filling 1 in the detected region, and generating a pressure significance region binary matrix BMHG.
2.3 pressure significance region image inpainting
And amplifying the BMHG to be consistent with the original image I in size by adopting a nearest neighbor interpolation method to obtain the IBMHG. Extraction of the pressure energy map significance region IP, ip=i&IBMHG, having a size of tm×tn,generating a mask matrix m from IP,/>
The IP is repaired by adopting an image repairing method, the repairing position is a point marked as 1 in M, and the image repairing is carried out by adopting a fast travelling method in the implementation, and the specific process is as follows: let us say the point IP we want to repair ab Abbreviated as ab, and its coordinates are (a, b). Selecting a 5x5 neighborhood B (epsilon) centered on (a, B), IP hl The point in B (. Epsilon.) is abbreviated as hl, and its coordinates are (h, l). And (2) represents the gradient direction, T is the distance from the pixel point to the neighborhood boundary, and N is the normal direction.
w(ab,hl)＝dir(ab,hl)·dst(ab,hl)·lev(ab,hl)
Updating the corresponding position value in the I through the repaired matrix IP, wherein the formula is as follows:
2.4 pressure significance region extraction
For updated matrix IAnd 2.1 and 2.2, regenerating a local mean matrix, a local entropy matrix and a pressure significance matrix. Calculating a pressure significance entropy matrix I' H ＝I H &BMHG, pressure significance mean matrix is I' M ＝I M &BMHG。
3. Building multi-resolution footprint energy map sets
Pair I S After each image in the image is processed according to the method in the step 2, multi-scale Gaussian blur is carried out, and a multi-resolution footprint pressure energy image group is generated
Wherein σ is the standard deviation of the Gaussian function, F is the number of scales, and σ=0 indicates that the original image is directly taken without Gaussian filtering. In practice, f=2, for I as in step 2 SR The operation is carried out on each image in the image sequence, and a local entropy matrix, a local mean matrix, a pressure significance entropy matrix and a pressure significance mean matrix are generated. Each image may generate I H ,I M ,I' H ,I' M "abbreviated { I } c C=1, 2,3,4}. Then multi-resolution footprint energy map set
4. Multi-resolution footprint energy map set matching score calculation
Each footprint sequence is represented by a multi-resolution footprint energy map set, q is the footprint sequence to be identified, and g is the database footprint sequence. The matching score simz among different footprint energy graphs is calculated, and then the different scores are weighted and fused by adopting a weighting coefficient omega obtained by a hinge loss function training method, so that a final matching score is obtained, namely:
score(q,g；ω)＝ω T S(q,g)
the simm is obtained by calculating the normalized cross-correlation of the corresponding matrix:
wherein R is represented byu, v represent the offsets of the abscissa and the ordinate of the energy image pixels, respectively, +.>R represents g At R q Mean value of the covered area, +.>R represents q At R g The mean value of the covered area, r, represents the resulting cross-correlation map.
simd is obtained by calculating the normalized Euclidean distance of the corresponding matrix:
the foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (3)
1. A multi-resolution shoe-wearing footprint sequence identification method based on pressure significance, which is characterized by comprising the following steps: an offline training process and an online recognition process;
the offline training process at least comprises the following steps:
acquiring a footprint pressure energy graph group; the constructing of the footprint pressure energy map set includes:
denoising the original footprint sequence image, and performing segmentation, clipping and weighted superposition on the horizontal projection of the sequence image to obtain a left gait energy image groupRight gait energy pattern group->Left stride energy map set->Right stride energy profile +.>Left step width energy map set->And right step width energy map group +.>The footprint pressure energy diagram group is I S ，/>；
Calculating local gray scale statistics of the footprint pressure energy map set: selectingThe single image in (a) is marked as +.>Will->The size divided into non-overlapping is +.>Is a local area block of (1)/(2)>And calculating the local information entropy H in each region block,
，
wherein,the gray value of the pixel in the representation area block is +.>Is a function of the probability of (1),
generatingIs a local entropy matrix I of (1) H ，
，
Calculating the gray average value in each region block to generateIs a local mean matrix I of (1) M ，/>；
Screening the pressure significance region of the footprint pressure energy map set;
repairing the pressure significance region of the footprint pressure energy map set;
extracting a pressure significance region of the footprint pressure energy map set;
constructing a multi-resolution footprint energy map set: for a pair ofThe local gray statistical features and the pressure significance areas of the footprint pressure energy image group are calculated and screened for each image, and a processed image is obtained;
performing multi-scale Gaussian blur on the processed image to generate a multi-resolution footprint pressure energy image group;
constructing a multi-resolution footprint energy graph group feature library;
the online identification process at least comprises the following steps:
acquiring a footprint pressure energy graph group;
calculating local gray scale statistical characteristics of the footprint pressure energy image group;
screening the pressure significance region of the footprint pressure energy map set;
repairing the pressure significance region of the footprint pressure energy map set;
extracting a pressure significance region of the footprint pressure energy map set;
constructing a multi-resolution footprint energy graph group;
and calculating the matching score of the multi-resolution footprint energy image group to be identified and the multi-resolution footprint energy image group feature library, thereby obtaining the identification result of the multi-resolution shoe-wearing footprint sequence.
2. The method of identifying a multi-resolution shoe-wear footprint sequence based on pressure saliency of claim 1, wherein the screening of pressure saliency areas comprises:
calculation ofMean of non-0 elements;
calculation ofMean of non-0 elements;
calculating significance entropy binary matrix；
Matrix is formedSum matrix->Performing AND operation according to elements to generate a matrix +.>；
For a pair ofPerforming Gaussian blur to obtain matrix +.>；
Computing a matrix；
For a pair ofPerforming contour detection, and filling 1 in the detected region to generate binary matrix of pressure significance region +.>。
3. The method of identifying a multi-resolution shoe-wear footprint sequence based on pressure saliency of claim 2, wherein patching the pressure saliency region of the footprint pressure energy map set comprises:
by adopting a nearest neighbor interpolation method, the methodMagnification to +.>The size is consistent and is->；
Extraction of regions of significance of a pressure energy map；
According toGenerating a mask matrix->；
Image restoration method pairRepairing the part of the body at the position +.>The point marked as 1 in the above is subjected to image restoration by adopting a rapid travelling method in the implementation;
through the repaired matrixFor->Updating the corresponding position value in the table;
the pressure saliency region of the footprint pressure energy map set is extracted.
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