CN113239796A - Parking lot entering method and system based on face recognition and computer storage medium - Google Patents

Parking lot entering method and system based on face recognition and computer storage medium Download PDF

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CN113239796A
CN113239796A CN202110515327.2A CN202110515327A CN113239796A CN 113239796 A CN113239796 A CN 113239796A CN 202110515327 A CN202110515327 A CN 202110515327A CN 113239796 A CN113239796 A CN 113239796A
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image
sub
face recognition
license plate
face
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张凯
刘杰
周建设
骆力明
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Capital Normal University
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Capital Normal University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/30—Individual registration on entry or exit not involving the use of a pass
    • G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
    • G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/625—License plates

Abstract

The method is characterized in that a parking lot entrance verification mode combining license plate recognition and face recognition is set, specifically, license plate recognition is preferentially carried out, when license plate image capturing fails or license plate recognition fails, face images continue to be captured, and calling is carried out based on S (2D)2The LPP face recognition algorithm carries out face recognition, and the entrance management of the vehicles is realized through the face recognition result, so that the defect that the vehicles cannot enter the parking lot or need manual check when the license plate is recognized wrongly is effectively overcome, and the efficiency of the entrance management of the parking lot is improved.

Description

Parking lot entering method and system based on face recognition and computer storage medium
Technical Field
The application relates to the technical field of parking lot management, in particular to a parking lot entering method and system based on face recognition and a computer storage medium.
Background
At present, more and more parking lots enter into a gateway and are provided with automatic license plate recognition equipment, but the license plate recognition accuracy rate still cannot be hundreds of percent, the identity of the vehicle still needs to be checked manually by workers when the recognition fails, the working efficiency is low, the single-vehicle inspection time is long, and the congestion of the entrance of the parking lot is easily caused when the vehicles are queued for security inspection for a large number of vehicles.
Therefore, how to improve the entrance efficiency of the parking lot is a current research focus. However, no breakthrough is made in the current state of the art.
Disclosure of Invention
In view of the above technical problems, the present application provides a parking lot entering method, system and computer storage medium based on face recognition.
A first aspect of the present application provides a parking lot entering method based on face recognition, where the method includes:
s1, shooting a preset area in front of a parking lot entrance barrier by a camera module, and monitoring whether a vehicle arrives;
s2, when the vehicle arrival is monitored, capturing a license plate image and transmitting the license plate image to a license plate recognition module for license plate recognition;
s3, if the grabbing fails or the recognition fails, executing the step S4;
s4, capturing a face image of the driver seat and transmitting the face image to a face recognition module;
s5, calling of the face recognition module is based on S (2D)2Carrying out face recognition by a face recognition algorithm of LPP;
and S6, if the face recognition is successful, opening a barrier to allow the vehicle to pass.
Preferably, the calling at the face recognition module is based on S (2D)2Before the face recognition algorithm of the LPP carries out face recognition, the method also comprises a training step:
dividing one face image in a training image set into m sub-images, wherein the sub-images form a sub-image set, each non-empty subset of the set is a sub-feature, so that one face image has n sub-features, and n is 2m-1；
The weight of the sub-features is calculated by a statistical method, and for a training gallery with s individuals and t images of each person, the specific steps are as follows:
(11) dividing the image into m sub-images to form n sub-featuresXi,i＝1,…，n；
(12) Each corresponding sub-image in the training image forms a sub-image set, a projection matrix of each sub-image is calculated, and a feature vector of the sub-image corresponding to each image is calculated;
(13) combining the feature vectors of the sub-images contained in each sub-feature into a large vector in order, and using the large vector as the feature vector of the sub-feature;
(14) and carrying out multiple groups of random detection, and counting the contribution of each sub-feature to classification as the weight of the sub-feature.
Preferably, the random detection method specifically comprises the following steps:
(21) training set s individuals, wherein each person randomly selects one image as a known image, and then randomly selects 30% of the rest images as unknown images;
(22) respectively representing the characteristics of the whole image by each sub-characteristic, calculating the distance between the unknown image and each known image, adopting a k-adjacent mode, and when the k images closest to the unknown image have the known images from the same person as the unknown image, calculating the counter sum of the sub-characteristiciAdding 1:
Figure BDA0003061539120000021
(23) after multiple groups of random detection are carried out, the weight of each sub-feature is calculated
Figure BDA0003061539120000022
Wherein, Count is the total number of analog detections.
Preferably, the face recognition module invoking in S5 is based on S (2D)2The LPP face recognition algorithm performs face recognition, and comprises the following steps:
(31) partitioning the known images, calculating the characteristics of each sub-image, and combining the characteristics into a characteristic vector of each sub-characteristic;
(32) for the unknown captured face image of the driver seat, firstly partitioning the image, and extracting the feature vector of the sub-features;
(33) calculating the distance between the corresponding sub-features of the unknown image and the known image, and adopting a k-neighborhood voting mode to obtain the weight wiThe ticket is cast to k known images with the closest distance to the ticket;
(34) counting the number of tickets obtained by each known image, and considering that the unknown image and the known image which has the most tickets are the same person.
Preferably, the features of the known image are extracted using the following formula:
Figure BDA0003061539120000031
preferably, said S (2D)2The projection formula of LPP is:
yi＝VTXiU
where V is the projection matrix in the image column direction and U is the projection matrix in the image row direction.
Preferably, in step S6, the face recognition result is stored in association with the vehicle image while the face recognition is successful and the barrier is opened for release.
The second aspect of the application also provides a parking lot entering system based on face recognition, and the system comprises a camera module, a license plate recognition module, a face recognition module and a control module;
the camera module is used for shooting a preset area in front of the parking lot entrance barrier gate, monitoring whether a vehicle arrives, and capturing a license plate image and transmitting the license plate image to the license plate recognition module when the vehicle arrives;
the license plate recognition module is used for receiving the license plate image transmitted by the camera module and recognizing the license plate;
the face recognition module is used for controlling the camera module to capture a face image of a driver seat when the camera module fails to capture or the license plate recognition module fails to recognize, calling a face recognition algorithm based on local preserving projection to perform face recognition, and transmitting a face recognition result to the control module;
and the control module is used for opening the barrier gate to release the barrier gate if the face recognition is successful.
A third aspect of the present application provides a parking lot entering device based on face recognition, which is characterized in that the device includes:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the parking lot entry method based on the face recognition.
A fourth aspect of the present application provides a storage medium, which is characterized in that the storage medium stores computer instructions, and when the computer instructions are called, the computer instructions are used for executing the parking lot entering method based on face recognition.
The invention has the beneficial effects that:
(1) the method is characterized in that a parking lot entrance verification mode combining license plate recognition and face recognition is set, specifically, license plate recognition is preferentially carried out, when license plate image capturing fails or license plate recognition fails, face images continue to be captured, and calling is carried out based on S (2D)2The LPP face recognition algorithm carries out face recognition, and the entrance management of the vehicles is realized through the face recognition result, so that the defect that the vehicles cannot enter the parking lot or need manual check when the license plate is recognized wrongly is effectively overcome, and the efficiency of the entrance management of the parking lot is improved.
(2) In the aspect of a face recognition algorithm, the method is based on the characteristics of LPP, a projection matrix is improved, the characteristics of a face image are extracted by adopting a supervised bidirectional two-dimensional LPP method, classification is carried out by using the sub-characteristic voting method provided by the method, and the method is more effective than the LPP method through experimental verification.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart of a parking lot entering method based on face recognition according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a parking lot entry system based on face recognition according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a parking lot entering device based on face recognition, disclosed in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present application, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which the present invention product is usually put into use, it is only for convenience of describing the present application and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and thus, should not be construed as limiting the present application.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart of a parking lot entering method based on face recognition according to an embodiment of the present application. As shown in fig. 1, a parking lot entering method based on face recognition in the embodiment of the present application includes:
s1, shooting a preset area in front of a parking lot entrance barrier by a camera module, and monitoring whether a vehicle arrives;
s2, when the vehicle arrival is monitored, capturing a license plate image and transmitting the license plate image to a license plate recognition module for license plate recognition;
s3, if the grabbing fails or the recognition fails, executing the step S4;
s4, capturing a face image of the driver seat and transmitting the face image to a face recognition module;
s5, calling of the face recognition module is based on S (2D)2Carrying out face recognition by a face recognition algorithm of LPP;
and S6, if the face recognition is successful, opening a barrier to allow the vehicle to pass.
In the embodiment of the application, the license plate is identified firstly, when the license plate image is failed to be captured or the license plate identification is failed, the face image is continuously captured, and a two-way two-dimensional local preserving projection method (S (2D) based on supervision is called2LPP) and realizes the entrance management of vehicles through the face recognition result, effectively avoids the defects that vehicles cannot enter the parking lot or need manual check when the license plate recognition is wrong, and improves the efficiency of the entrance management of the parking lot.
In this alternative embodiment, the partial preserving projection algorithm is a two-way two-dimensional partial preserving projection algorithm (S (2D)2LPP)。
In this optional embodiment, before the face recognition module performs feature extraction on the face image by using a local preserving projection algorithm, the method further includes a training step:
dividing one face image in a training image set into m sub-images, wherein the sub-images form a sub-image set, each non-empty subset of the set is a sub-feature, so that one face image has n sub-features, and n is 2m-1；
The weight of the sub-features is calculated by a statistical method, and for a training gallery with s individuals and t images of each person, the specific steps are as follows:
(11) dividing the image into m sub-images to form n sub-features Xi,i＝1，…，n；
(12) Each corresponding sub-image in the training image forms a sub-image set, a projection matrix of each sub-image is calculated, and a feature vector of the sub-image corresponding to each image is calculated;
(13) combining the feature vectors of the sub-images contained in each sub-feature into a large vector in order, and using the large vector as the feature vector of the sub-feature;
(14) and carrying out multiple groups of random detection, and counting the contribution of each sub-feature to classification as the weight of the sub-feature.
In this optional embodiment, the specific process of the random detection method is as follows:
(21) training set s individuals, wherein each person randomly selects one image as a known image, and then randomly selects 30% of the rest images as unknown images;
(22) respectively representing the characteristics of the whole image by each sub-characteristic, calculating the distance between the unknown image and each known image, adopting a k-adjacent mode, and when the k images closest to the unknown image have the known images from the same person as the unknown image, calculating the counter sum of the sub-characteristiciAdding 1:
Figure BDA0003061539120000071
(23) after multiple groups of random detection are carried out, the weight of each sub-feature is calculated
Figure BDA0003061539120000072
Wherein, Count is the total number of analog detections.
In this optional embodiment, the face recognition module performs feature extraction on the facial image by using a local preserving projection algorithm, and performs classification by using a nearest neighbor classifier, including the following steps:
(31) partitioning the known images, calculating the characteristics of each sub-image, and combining the characteristics into a characteristic vector of each sub-characteristic;
(32) for an unknown image to be detected, firstly, partitioning the image, and extracting a feature vector of a sub-feature;
(33) calculating the distance between the corresponding sub-features of the unknown image and the known image, and adopting a k-neighborhood voting mode to obtain the weight wiThe ticket is cast to k known images with the closest distance to the ticket;
(34) counting the number of tickets obtained by each known image, and considering that the unknown image and the known image which has the most tickets are the same person.
In this alternative embodiment, the features of the known image are extracted using the following formula:
Figure BDA0003061539120000081
in this alternative embodiment, said S (2D)2The projection formula of LPP is:
yi＝VTXiU
where V is the projection matrix in the image column direction and U is the projection matrix in the image row direction.
Preferably, in step S6, the face recognition result is stored in association with the vehicle image while the face recognition is successful and the barrier is opened for release.
In the embodiment of the application, under the condition that the license plate recognition fails, in order to continuously realize the purpose of information management of the entering vehicle, the face recognition result and the vehicle image are stored in a correlation mode, and subsequent checking is facilitated. Of course, those skilled in the art can also select and store other information, such as entry time, entry number, etc., in association according to design requirements, which is not limited in the present application.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a parking lot entry system based on face recognition according to an embodiment of the present application. As shown in fig. 2, the parking lot entry system based on face recognition in the embodiment of the present application includes a camera module, a license plate recognition module, a face recognition module, and a control module;
the camera module is used for shooting a preset area in front of the parking lot entrance barrier gate, monitoring whether a vehicle arrives, and capturing a license plate image and transmitting the license plate image to the license plate recognition module when the vehicle arrives;
the license plate recognition module is used for receiving the license plate image transmitted by the camera module and recognizing the license plate;
the face recognition module is used for controlling the camera module to capture a face image of a driver seat when the camera module fails to capture or the license plate recognition module fails to recognize, calling a face recognition algorithm based on local preserving projection to perform face recognition, and transmitting a face recognition result to the control module;
and the control module is used for opening the barrier gate to release the barrier gate if the face recognition is successful.
In the embodiment of the application, the license plate is identified firstly, when the license plate image is failed to be captured or the license plate identification is failed, the face image is continuously captured, and a two-way two-dimensional local preserving projection method (S (2D) based on supervision is called2LPP) face recognition algorithm for face recognition and face recognition resultThe vehicle entering management is realized, the defects that the vehicle cannot enter the parking lot or needs manual check when the license plate is identified wrongly are effectively overcome, and the parking lot entering management efficiency is improved.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of a parking lot entering device based on face recognition according to an embodiment of the present application. As shown in fig. 3, the parking lot entering device based on face recognition in the embodiment of the present application is characterized in that the device includes:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the parking lot entry method based on the face recognition.
Example four
An embodiment of the present application provides a storage medium, where the storage medium stores a computer instruction, and the computer instruction is used to execute the parking lot entering method based on face recognition as described above when being called.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A parking lot entering method based on face recognition is characterized in that: the method comprises the following steps:
s1, shooting a preset area in front of a parking lot entrance barrier by a camera module, and monitoring whether a vehicle arrives;
s2, when the vehicle arrival is monitored, capturing a license plate image and transmitting the license plate image to a license plate recognition module for license plate recognition;
s3, if the grabbing fails or the recognition fails, executing the step S4;
s4, capturing a face image of the driver seat and transmitting the face image to a face recognition module;
s5, calling of the face recognition module is based on S (2D)2Carrying out face recognition by a face recognition algorithm of LPP;
and S6, if the face recognition is successful, opening a barrier to allow the vehicle to pass.
2. The method of claim 1, wherein: invoking a face recognition module based on S (2D)2Before the face recognition algorithm of the LPP carries out face recognition, the method also comprises a training step:
dividing one face image in a training image set into m sub-images, wherein the sub-images form a sub-image set, each non-empty subset of the set is a sub-feature, so that one face image has n sub-features, and n is 2m-1；
The weight of the sub-features is calculated by a statistical method, and for a training gallery with s individuals and t images of each person, the specific steps are as follows:
(11) dividing the image into m sub-images to form n sub-features Xi,i＝1,…,n；
(12) Each corresponding sub-image in the training image forms a sub-image set, a projection matrix of each sub-image is calculated, and a feature vector of the sub-image corresponding to each image is calculated;
(13) combining the feature vectors of the sub-images contained in each sub-feature into a large vector in order, and using the large vector as the feature vector of the sub-feature;
(14) and carrying out multiple groups of random detection, and counting the contribution of each sub-feature to classification as the weight of the sub-feature.
3. The method of claim 2, wherein: the random detection method comprises the following specific processes:
(21) training set s individuals, wherein each person randomly selects one image as a known image, and then randomly selects 30% of the rest images as unknown images;
(22) using each sub-feature separatelyRepresenting the characteristics of the whole image, calculating the distance between the unknown image and each known image, adopting a k-adjacent mode, and when the k images closest to the unknown image have the known images from the same person as the unknown image, calculating the counter sum of the sub-characteristicsiAdding 1:
Figure FDA0003061539110000021
(23) after multiple groups of random detection are carried out, the weight of each sub-feature is calculated
Figure FDA0003061539110000022
Wherein, Count is the total number of analog detections.
4. The method of claim 1, wherein: the face recognition module in S5 calls based on S (2D)2The LPP face recognition algorithm performs face recognition, and comprises the following steps:
(31) partitioning the known images, calculating the characteristics of each sub-image, and combining the characteristics into a characteristic vector of each sub-characteristic;
(32) for the unknown captured face image of the driver seat, firstly partitioning the image, and extracting the feature vector of the sub-features;
(33) calculating the distance between the corresponding sub-features of the unknown image and the known image, and adopting a k-neighborhood voting mode to obtain the weight wiThe ticket is cast to k known images with the closest distance to the ticket;
(34) counting the number of tickets obtained by each known image, and considering that the unknown image and the known image which has the most tickets are the same person.
5. The method of claim 4, wherein: the features of the known image are extracted using the following formula:
Figure FDA0003061539110000023
6. the method according to any one of claims 1 to 5, wherein: the S (2D)2The projection formula of LPP is:
yi＝VTXiU
where V is the projection matrix in the image column direction and U is the projection matrix in the image row direction.
7. The method of claim 1, wherein: in step S6, the face recognition is successful and the barrier is opened for release, and the face recognition result is stored in association with the vehicle image.
8. The utility model provides a parking area system of entering based on face identification which characterized in that: the system comprises a camera module, a license plate recognition module, a face recognition module and a control module;
the camera module is used for shooting a preset area in front of the parking lot entrance barrier gate, monitoring whether a vehicle arrives, and capturing a license plate image and transmitting the license plate image to the license plate recognition module when the vehicle arrives;
the license plate recognition module is used for receiving the license plate image transmitted by the camera module and recognizing the license plate;
the face recognition module is used for controlling the camera module to capture a face image of a driver seat when the camera module fails to capture or the license plate recognition module fails to recognize, calling a face recognition algorithm based on local preserving projection to perform face recognition, and transmitting a face recognition result to the control module;
and the control module is used for opening the barrier gate to release the barrier gate if the face recognition is successful.
9. A parking lot entry device based on face recognition, the device comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the face recognition-based parking lot entry method according to any one of claims 1 to 7.
10. A storage medium storing computer instructions which, when invoked, perform a method for parking lot entry based on face recognition according to any one of claims 1 to 7.
CN202110515327.2A 2021-05-12 2021-05-12 Parking lot entering method and system based on face recognition and computer storage medium Pending CN113239796A (en)

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Application publication date: 20210810