CN111401291A - Stranger identification method and device - Google Patents
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- CN111401291A CN111401291A CN202010215486.6A CN202010215486A CN111401291A CN 111401291 A CN111401291 A CN 111401291A CN 202010215486 A CN202010215486 A CN 202010215486A CN 111401291 A CN111401291 A CN 111401291A
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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
- G06V40/172—Classification, e.g. identification
- G06V40/173—Classification, e.g. identification face re-identification, e.g. recognising unknown faces across different face tracks
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides a stranger identification method and device, which comprise the following steps: acquiring a face image to be recognized, and extracting the face characteristics of the face image to be recognized; respectively calculating the human face characteristics and the characteristics of human face images prestored in a database to obtain a plurality of similarity degrees; arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees; calculating similarity statistic according to the arranged similarity; selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer; obtaining a recognition result by the face feature, the similarity statistic and the N similarities through a neural network model; the type of the face image to be recognized is determined according to the recognition result, so that the speed and the accuracy of face recognition can be improved.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to a method and a device for recognizing strangers.
Background
Personnel attendance, visitor registration and stranger identification are the main problems of personnel management and control in intelligent park construction, wherein the personnel attendance and the visitor registration are already well solved by face recognition technology due to business characteristics, and the stranger identification problem is not effectively solved due to non-matching characteristics.
Generally speaking, a stranger can be defined as a person who is not registered in a specific area, and stranger identification is to timely and effectively find the stranger entering a garden and form a behavior track of the stranger in the garden, so that the purposes of effectively early warning before a problem occurs and effectively tracing after the problem occurs are achieved.
At present, a stranger identification method is to match a face image meeting face identification with a registry, and if the matching is unsuccessful, the stranger is judged, but the method has strong subjective factors and low identification precision. In addition, a method based on clustering and multi-strategy fusion is adopted to identify strangers, and the method has delay and low speed of identifying the face images.
In conclusion, the above methods cannot be used for accurately identifying strangers.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for recognizing a stranger, which can improve the speed and accuracy of face recognition.
In a first aspect, an embodiment of the present invention provides a stranger identification method, where the method includes:
acquiring a face image to be recognized, and extracting the face features of the face image to be recognized;
respectively calculating the human face features and the features of human face images prestored in a database to obtain a plurality of similarity degrees;
arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees;
calculating similarity statistic according to the arranged similarity;
selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer;
obtaining an identification result by passing the face features, the similarity statistic and the N similarities through a neural network model;
and determining the category of the face image to be recognized according to the recognition result.
Further, obtaining a recognition result by using the face features, the similarity statistic and the N similarities through a neural network model, including:
reducing the dimension of the face features through a first full connection layer and a second full connection layer respectively to obtain the face features after dimension reduction;
splicing the face features subjected to dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector;
and sequentially passing the one-dimensional vector through a third full-connection layer and a classification layer to obtain the identification result.
Further, the splicing the face features after the dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector includes:
averaging the arranged similarities to obtain an average value;
selecting a middle value from the arranged similarity;
and splicing the face features subjected to dimension reduction, the average value, the intermediate value and the N similarity degrees to obtain the one-dimensional vector.
Further, the determining the type of the facial image to be recognized according to the recognition result includes:
when the first numerical value is larger than the second numerical value, the category of the face image to be recognized is a stranger;
and when the second numerical value is larger than the first numerical value, the category of the facial image to be recognized is a non-stranger.
In a second aspect, an embodiment of the present invention provides an apparatus for identifying a stranger, where the apparatus includes:
the device comprises a to-be-recognized face image acquisition unit, a face recognition unit and a face recognition unit, wherein the to-be-recognized face image acquisition unit is used for acquiring a to-be-recognized face image and extracting the face features of the to-be-recognized face image;
the similarity calculation unit is used for respectively calculating the human face features and the features of the human face images prestored in the database to obtain a plurality of similarities;
the arrangement unit is used for arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees;
a similarity statistic unit for calculating similarity statistic according to the arranged similarity;
a selecting unit, configured to select N similarity degrees from the arranged similarity degrees, where N is a positive integer;
the recognition result acquisition unit is used for acquiring a recognition result by the human face features, the similarity statistic and the N similarities through a neural network model;
and the determining unit is used for determining the category of the face image to be recognized according to the recognition result.
Further, the identification result obtaining unit is specifically configured to:
reducing the dimension of the face features through a first full connection layer and a second full connection layer respectively to obtain the face features after dimension reduction;
splicing the face features subjected to dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector;
and sequentially passing the one-dimensional vector through a third full-connection layer and a classification layer to obtain the identification result.
Further, the identification result obtaining unit is specifically configured to:
averaging the arranged similarities to obtain an average value;
selecting a middle value from the arranged similarity;
and splicing the face features subjected to dimension reduction, the average value, the intermediate value and the N similarity degrees to obtain the one-dimensional vector.
Further, the recognition result includes a first numerical value and a second numerical value, the category corresponding to the first numerical value is confidence of strangers, the category corresponding to the second numerical value is confidence of non-strangers, and the determining unit is specifically configured to:
when the first numerical value is larger than the second numerical value, the category of the face image to be recognized is a stranger;
and when the second numerical value is larger than the first numerical value, the category of the facial image to be recognized is a non-stranger.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor implements the method described above when executing the computer program.
In a fourth aspect, embodiments of the invention provide a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method as described above.
The embodiment of the invention provides a stranger identification method and device, which comprises the following steps: acquiring a face image to be recognized, and extracting the face characteristics of the face image to be recognized; respectively calculating the human face characteristics and the characteristics of human face images prestored in a database to obtain a plurality of similarity degrees; arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees; calculating similarity statistic according to the arranged similarity; selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer; obtaining a recognition result by the face feature, the similarity statistic and the N similarities through a neural network model; the type of the face image to be recognized is determined according to the recognition result, so that the speed and the accuracy of face recognition can be improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a stranger identification method according to an embodiment of the present invention;
fig. 2 is a flowchart of step S106 in the stranger identification method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a neural network model according to an embodiment of the present invention;
fig. 4 is a schematic view of an identification device for strangers according to a second embodiment of the present invention.
Icon:
1-a face image acquisition unit to be recognized; 2-a similarity calculation unit; 3-an alignment unit; 4-similarity statistic unit; 5-selecting a unit; 6-a recognition result acquisition unit; 7-determining unit.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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 invention.
For the understanding of the present embodiment, the following detailed description will be given of the embodiment of the present invention.
The first embodiment is as follows:
fig. 1 is a flowchart of a stranger identification method according to an embodiment of the present invention.
Referring to fig. 1, the method includes the steps of:
step S101, acquiring a face image to be recognized, and extracting the face characteristics of the face image to be recognized;
here, when the face image to be recognized is acquired, the face image to be recognized is input into the face identification model, thereby outputting the face feature.
Step S102, calculating the human face characteristics and the characteristics of the human face images prestored in the database respectively to obtain a plurality of similarity degrees;
when the training samples are generated, a large number of pre-stored face images in the database are randomly selected, and the face features and the features of the pre-stored face images are respectively calculated to obtain a plurality of similarity degrees. By selecting a large number of training samples, overfitting can be effectively prevented.
Step S103, arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees;
step S104, calculating similarity statistic according to the arranged similarity;
s105, selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer;
s106, obtaining a recognition result by the face feature, the similarity statistic and the N similarities through a neural network model;
and step S107, determining the type of the face image to be recognized according to the recognition result.
Further, referring to fig. 2, step S106 includes the steps of:
step S201, dimension reduction is carried out on the face features through a first full connection layer and a second full connection layer respectively, and the face features after dimension reduction are obtained;
step S202, splicing the face features after dimension reduction, the similarity statistic and N similarities to obtain a one-dimensional vector;
and step S203, enabling the one-dimensional vector to sequentially pass through a third full-connection layer and a classification layer to obtain an identification result.
Specifically, referring to fig. 3, when a face image to be recognized is acquired, the face image to be recognized is input into a face identification model, so that face features are output. Because the dimension of the face features is relatively high, dimension reduction processing needs to be performed on the face features. The number of nodes of the first fully-connected layer is 128, and the number of nodes of the second fully-connected layer is 32. And obtaining the face features after dimension reduction after the face features pass through the first full connection layer and the second full connection layer. And splicing the face features after dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector. Inputting the one-dimensional vectors obtained by splicing into a third full-connection layer, and finally inputting into a classification layer, thereby obtaining an identification result, wherein the identification result is a two-dimensional vector comprising a first numerical value and a second numerical value.
Further, step S202 includes the steps of:
step S301, averaging the arranged similarity to obtain an average value;
step S302, selecting a middle value from the arranged similarity;
and step S303, splicing the face features, the average value, the intermediate value and the N similarity after dimension reduction to obtain a one-dimensional vector.
Specifically, the averaging method is to average all the arranged similarities to obtain an average value; the method of selecting the median is to select the median from the ranked similarities. For example, the degree of similarity after alignment is a1, a2, A3, a4, a5, with an intermediate value of A3. And then splicing the face features, the average value, the intermediate value and the N similarity after dimension reduction to obtain a one-dimensional vector.
In addition, in the splicing process, the mean value and the median value are not limited to be spliced, the variance can be obtained according to the similarity, and the face features, the mean value, the median value, the variance and the N similarity after dimension reduction are spliced, so that a one-dimensional vector is obtained.
Further, the recognition result includes a first value and a second value, the category corresponding to the first value is confidence of strangers, and the category corresponding to the second value is confidence of non-strangers, and the step S107 includes:
when the first numerical value is larger than the second numerical value, the type of the face image to be recognized is a stranger;
and when the second numerical value is larger than the first numerical value, the category of the face image to be recognized is a non-stranger.
The embodiment of the invention provides a stranger identification method, which comprises the following steps: acquiring a face image to be recognized, and extracting the face characteristics of the face image to be recognized; respectively calculating the human face characteristics and the characteristics of human face images prestored in a database to obtain a plurality of similarity degrees; arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees; calculating similarity statistic according to the arranged similarity; selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer; obtaining a recognition result by the face feature, the similarity statistic and the N similarities through a neural network model; the type of the face image to be recognized is determined according to the recognition result, so that the speed and the accuracy of face recognition can be improved.
Example two:
fig. 4 is a schematic view of an identification device for strangers according to a second embodiment of the present invention.
Referring to fig. 4, the apparatus includes:
the face image acquiring unit 1 is used for acquiring a face image to be recognized and extracting face features of the face image to be recognized;
the similarity calculation unit 2 is used for calculating the human face features and the features of the human face images prestored in the database respectively to obtain a plurality of similarities;
the arrangement unit 3 is used for arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees;
a similarity statistic unit 4 for calculating similarity statistic according to the arranged similarities;
a selecting unit 5, configured to select N similarity degrees from the arranged similarity degrees, where N is a positive integer;
the recognition result acquisition unit 6 is used for acquiring a recognition result by the face features, the similarity statistic and the N similarities through a neural network model;
and the determining unit 7 is used for determining the category of the face image to be recognized according to the recognition result.
Further, the identification result obtaining unit 6 is specifically configured to:
reducing the dimension of the face features through a first full connection layer and a second full connection layer respectively to obtain the face features after dimension reduction;
splicing the face features after dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector;
and (5) enabling the one-dimensional vector to pass through a third full-connection layer and a classification layer in sequence to obtain an identification result.
Further, the recognition result obtaining unit 6 is specifically configured to:
averaging the arranged similarity to obtain an average value;
selecting a middle value from the arranged similarity;
and splicing the face features, the average value, the intermediate value and the N similarity after dimension reduction to obtain the one-dimensional vector.
Further, the recognition result includes a first numerical value and a second numerical value, the category corresponding to the first numerical value is confidence of strangers, the category corresponding to the second numerical value is confidence of non-strangers, and the determining unit 7 is specifically configured to:
when the first numerical value is larger than the second numerical value, the type of the face image to be recognized is a stranger;
and when the second numerical value is larger than the first numerical value, the category of the face image to be recognized is a non-stranger.
The embodiment of the invention provides a stranger identification device, which comprises: acquiring a face image to be recognized, and extracting the face characteristics of the face image to be recognized; respectively calculating the human face characteristics and the characteristics of human face images prestored in a database to obtain a plurality of similarity degrees; arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees; calculating similarity statistic according to the arranged similarity; selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer; obtaining a recognition result by the face feature, the similarity statistic and the N similarities through a neural network model; the type of the face image to be recognized is determined according to the recognition result, so that the speed and the accuracy of face recognition can be improved.
The embodiment of the invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps of the stranger identification method provided by the embodiment are realized when the processor executes the computer program.
The embodiment of the present invention further provides a computer-readable medium having a non-volatile program code executable by a processor, where the computer-readable medium stores a computer program, and the computer program is executed by the processor to perform the steps of the stranger identification method according to the above-mentioned embodiment.
The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method of identifying a stranger, the method comprising:
acquiring a face image to be recognized, and extracting the face features of the face image to be recognized;
respectively calculating the human face features and the features of human face images prestored in a database to obtain a plurality of similarity degrees;
arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees;
calculating similarity statistic according to the arranged similarity;
selecting N similarity degrees from the arranged similarity degrees, wherein N is a positive integer;
obtaining an identification result by passing the face features, the similarity statistic and the N similarities through a neural network model;
and determining the category of the face image to be recognized according to the recognition result.
2. The stranger recognition method of claim 1, wherein the passing the face features, the similarity statistic and the N similarities through a neural network model to obtain a recognition result comprises:
reducing the dimension of the face features through a first full connection layer and a second full connection layer respectively to obtain the face features after dimension reduction;
splicing the face features subjected to dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector;
and sequentially passing the one-dimensional vector through a third full-connection layer and a classification layer to obtain the identification result.
3. The stranger identification method according to claim 2, wherein the stitching the face features subjected to the dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector comprises:
averaging the arranged similarities to obtain an average value;
selecting a middle value from the arranged similarity;
and splicing the face features subjected to dimension reduction, the average value, the intermediate value and the N similarity degrees to obtain the one-dimensional vector.
4. The stranger recognition method according to claim 1, wherein the recognition result comprises a first numerical value and a second numerical value, the first numerical value corresponds to a confidence level of a stranger, the second numerical value corresponds to a confidence level of a non-stranger, and the determining the category of the face image to be recognized according to the recognition result comprises:
when the first numerical value is larger than the second numerical value, the category of the face image to be recognized is a stranger;
and when the second numerical value is larger than the first numerical value, the category of the facial image to be recognized is a non-stranger.
5. An identification device for a stranger, the device comprising:
the device comprises a to-be-recognized face image acquisition unit, a face recognition unit and a face recognition unit, wherein the to-be-recognized face image acquisition unit is used for acquiring a to-be-recognized face image and extracting the face features of the to-be-recognized face image;
the similarity calculation unit is used for respectively calculating the human face features and the features of the human face images prestored in the database to obtain a plurality of similarities;
the arrangement unit is used for arranging the plurality of similarity degrees in a descending order to obtain the arranged similarity degrees;
a similarity statistic unit for calculating similarity statistic according to the arranged similarity;
a selecting unit, configured to select N similarity degrees from the arranged similarity degrees, where N is a positive integer;
the recognition result acquisition unit is used for acquiring a recognition result by the human face features, the similarity statistic and the N similarities through a neural network model;
and the determining unit is used for determining the category of the face image to be recognized according to the recognition result.
6. The stranger identification device according to claim 5, wherein the identification result obtaining unit is specifically configured to:
reducing the dimension of the face features through a first full connection layer and a second full connection layer respectively to obtain the face features after dimension reduction;
splicing the face features subjected to dimension reduction, the similarity statistic and the N similarities to obtain a one-dimensional vector;
and sequentially passing the one-dimensional vector through a third full-connection layer and a classification layer to obtain the identification result.
7. The stranger identification device according to claim 6, wherein the identification result obtaining unit is specifically configured to:
averaging the arranged similarities to obtain an average value;
selecting a middle value from the arranged similarity;
and splicing the face features subjected to dimension reduction, the average value, the intermediate value and the N similarity degrees to obtain the one-dimensional vector.
8. The stranger identification device according to claim 5, wherein the identification result includes a first numerical value and a second numerical value, the first numerical value corresponds to a confidence level of a stranger, the second numerical value corresponds to a confidence level of a non-stranger, and the determining unit is specifically configured to:
when the first numerical value is larger than the second numerical value, the category of the face image to be recognized is a stranger;
and when the second numerical value is larger than the first numerical value, the category of the facial image to be recognized is a non-stranger.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 4 when executing the computer program.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1 to 4.
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| CN113469033A (en) * | 2021-06-30 | 2021-10-01 | 北京集创北方科技股份有限公司 | Image recognition method and device, electronic equipment and storage medium |
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