CN111242067A - Face recognition system - Google Patents
Face recognition system Download PDFInfo
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- CN111242067A CN111242067A CN202010055048.8A CN202010055048A CN111242067A CN 111242067 A CN111242067 A CN 111242067A CN 202010055048 A CN202010055048 A CN 202010055048A CN 111242067 A CN111242067 A CN 111242067A
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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
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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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
Abstract
The invention discloses a face recognition system, which comprises a front end and a rear end, wherein the front end comprises a snapshot camera or a recognition camera, a face NVR and a display panel; the back end comprises a directory server, a cloud comparison algorithm server, a cloud control deployment server, a storage server and a face recognition system. The invention can be applied to face recognition in the environment of mixed pedestrian and vehicle running, and can be used in the environment with large pedestrian flow without causing congestion; the pedestrian can finish the face recognition only by normally walking in the monitoring range of the face recognition camera and not intentionally shielding or avoiding the snapshot recognition of the camera, thereby perfectly solving the problems; and front end equipment only has a camera and results to show dull and stereotypedly or add the construction of a human face NVR face identification front end hardware of front end can be realized, has reduced the front end and has built the construction degree of difficulty of platform, compares the dull and stereotyped + the mode of passageway floodgate of face identification simultaneously greatly to practice thrift the cost.
Description
Technical Field
The invention relates to the field of face recognition systems, in particular to a face recognition system.
Background
International authoritative Market insight report Gen Market instruments published on the near day "global face recognition equipment Market research report" is said that the face recognition output value in 2017 in China accounts for 29.29% of the Market share in the world, and will reach 44.59% in 2023. The face recognition is a field with rapid AI technology development and more applications, and the face recognition in China is widely applied at present and accumulates a plurality of actual combat experiences
However, the main application of face recognition in the current market requires people to actively cooperate with each other to complete face recognition comparison, and the main application scenario is to use the face recognition in combination with a gateway gate, for example, a high-speed rail passes through the gate; the method has the advantages of high accuracy and low error recognition rate, but has the considerable defects of requiring active cooperation of personnel and being not suitable for use in the traffic environment of mixed pedestrian and vehicle, and easily causing congestion when the traffic is large, such as schools.
Disclosure of Invention
The technical problem to be solved by the invention is a face recognition system capable of solving the above problems.
The invention is realized by the following technical scheme: a face recognition system characterized by: the system comprises a front end and a rear end, wherein the front end comprises a snapshot camera or an identification camera, a human face NVR and a display panel; the back end comprises a directory server, a cloud comparison algorithm server, a cloud control deployment server, a storage server and a face recognition system; the snapshot camera or the recognition camera automatically takes a snapshot to generate data after detecting a face or a human figure, the generated data is transmitted back to the cloud server through three types of return data, and a comparison processing result is transmitted to the face recognition attendance system, the face recognition attendance system generates a face recognition running water record after processing the data through the setting of a user, generates a corresponding report and pushes the information to a front-end display panel;
the return data return mode comprises the following steps:
the method comprises the following steps: in order to collect face data in advance, the face data are deployed and issued to a comparison algorithm server through a cloud control deployment server, and modeling processing is carried out on each face data; the method comprises the following steps that after a snapshot camera at the front end detects a human face or a human figure, face data are automatically captured and transmitted back to a cloud comparison algorithm server through a directory server, the comparison algorithm server compares one or more human faces of which the snapshot data are preprocessed with face information which is issued and successfully modeled, and transmits a comparison result to a cloud control deployment server;
the second method comprises the following steps: face data are collected in advance, deployed and issued to a front-end data comparison NVR through a cloud control deployment server, and modeling processing is carried out on each face data; after detecting a face or a human figure, the front-end snapshot camera automatically shoots the face data, directly returns the face data to the NVR for comparison through preprocessing, and transmits a comparison result to the cloud control deployment server through the directory server;
the third method comprises the following steps: the method comprises the steps that face data are collected in advance, deployed and issued to a face recognition camera with an algorithm and a storage function at the front end through a cloud control deployment server, and modeling processing is carried out on each face data; the front-end face recognition camera automatically captures face data after detecting a face or a human figure, then directly compares the face data, and transmits a comparison result to the cloud control deployment server through the directory server.
The invention has the beneficial effects that: the invention can be applied to face recognition in the environment of mixed pedestrian and vehicle running, and can be used in the environment with large pedestrian flow without causing congestion; the pedestrian can finish the face recognition only by normally walking in the monitoring range of the face recognition camera and not intentionally shielding or avoiding the snapshot recognition of the camera, thereby perfectly solving the problems; and front end equipment only has a camera and results to show dull and stereotypedly or add the construction of a human face NVR face identification front end hardware of front end can be realized, has reduced the front end and has built the construction degree of difficulty of platform, compares the dull and stereotyped + the mode of passageway floodgate of face identification simultaneously greatly to practice thrift the cost.
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a system diagram of the present invention;
FIG. 2 is a basic flow diagram of the present invention;
FIG. 3 is a flowchart of a first embodiment of the present invention;
FIG. 4 is a flowchart of a second embodiment of the present invention;
FIG. 5 is a flowchart of a third embodiment of the present invention.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
In the description of the present invention, it is to be understood that the terms "one end", "the other end", "outside", "upper", "inside", "horizontal", "coaxial", "central", "end", "length", "outer end", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
Further, in the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
The use of terms such as "upper," "above," "lower," "below," and the like in describing relative spatial positions herein is for the purpose of facilitating description to describe one element or feature's relationship to another element or feature as illustrated in the figures. The spatially relative positional terms may be intended to encompass different orientations of the device 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 "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the exemplary term "below" can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In the present invention, unless otherwise explicitly specified or limited, the terms "disposed," "sleeved," "connected," "penetrating," "plugged," and the like are to be construed broadly, e.g., as a fixed connection, a detachable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1 to 2, a face recognition system includes a front end and a back end, the front end includes a snapshot camera or a recognition camera, a face NVR, and a display panel; the back end comprises a directory server, a cloud comparison algorithm server, a cloud control deployment server, a storage server and a face recognition system; the automatic snapshot produces data after snapshot camera or recognition camera detects face or humanoid, the data of production is back transmitted to the high in the clouds server through three kinds of passback data, and transmit the processing result of comparison to face identification attendance system, face identification attendance system generates face identification running water record after processing data through the setting of user, and generate corresponding statement, and to the display panel of information propelling movement front end and the little letter of paying close attention to this personnel, camera passback data is as follows to the three kinds of modes in the high in the clouds:
the first embodiment is as follows: as shown in fig. 3, face data are collected in advance, deployed and issued to a comparison algorithm server through a cloud control deployment server, and modeling processing is performed on each face data; the method comprises the steps that after a face or a human figure is detected by a snapshot camera at the front end, face data are automatically captured and transmitted back to a cloud comparison algorithm server through a directory server, the comparison algorithm server compares one or more faces of which the snapshot data are preprocessed with face information which is issued and successfully modeled, and transmits comparison results to a cloud control deployment server.
Example two: as shown in fig. 4, face data is collected in advance, deployed and issued to the front-end data comparison NVR through the cloud control deployment server, and modeling processing is performed on each face data; after detecting the face or the human figure, the front-end snapshot camera automatically shoots the face data, directly returns the face data to the NVR for comparison through preprocessing, and transmits the comparison result to the cloud control deployment server through the directory server.
Example three: as shown in fig. 5, face data are collected in advance, deployed and issued to a face recognition camera with an algorithm and a storage function at the front end through a cloud control deployment server, and modeling processing is performed on each face data; the front-end face recognition camera automatically captures face data after detecting a face or a human figure, then directly compares the face data, and transmits a comparison result to the cloud control deployment server through the directory server.
The device can be applied to face recognition in the environment of mixed pedestrian and vehicle running, and can be used in the environment with large pedestrian flow without causing congestion; the pedestrian only needs to normally walk in the monitoring range of the face recognition camera and does not intentionally shield or avoid the snapshot recognition of the camera, so that the face recognition can be completed, and the problems are perfectly solved. And front end equipment only has a camera and results to show dull and stereotypedly or add the construction of a human face NVR face identification front end hardware of front end can be realized, has reduced the front end and has built the construction degree of difficulty of platform, compares the dull and stereotyped + the mode of passageway floodgate of face identification simultaneously greatly to practice thrift the cost.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that are not thought of through the inventive work should be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (4)
1. A face recognition system characterized by: the system comprises a front end and a rear end, wherein the front end comprises a snapshot camera or an identification camera, a human face NVR and a display panel; the back end comprises a directory server, a cloud comparison algorithm server, a cloud control deployment server, a storage server and a face recognition system; the snapshot camera or the recognition camera automatically takes a snapshot to generate data after detecting a human face or a human figure, the generated data is transmitted back to the cloud server in a data return mode, a comparison processing result is transmitted to the human face recognition attendance system, the human face recognition attendance system generates a human face recognition running water record after processing the data through the setting of a user, generates a corresponding report and pushes the information to the display panel at the front end.
2. The face recognition system of claim 1, wherein: the data is returned in a mode that face data are collected in advance, deployed and issued to a comparison algorithm server through a cloud control deployment server, and modeling processing is carried out on each face data; the method comprises the steps that after a face or a human figure is detected by a snapshot camera at the front end, face data are automatically captured and transmitted back to a cloud comparison algorithm server through a directory server, the comparison algorithm server compares one or more faces of which the snapshot data are preprocessed with face information which is issued and successfully modeled, and transmits comparison results to a cloud control deployment server.
3. The face recognition system of claim 1, wherein: the data is returned in a mode that face data are collected in advance, deployed and issued to a front-end data comparison NVR through a cloud control deployment server, and modeling processing is carried out on each face data; after detecting the face or the human figure, the front-end snapshot camera automatically shoots the face data, directly returns the face data to the NVR for comparison through preprocessing, and transmits the comparison result to the cloud control deployment server through the directory server.
4. The face recognition system of claim 1, wherein: the data is returned in a mode that face data are collected in advance, deployed and issued to a face recognition camera with an algorithm and a storage function at the front end through a cloud control deployment server, and modeling processing is carried out on each face data; the front-end face recognition camera automatically captures face data after detecting a face or a human figure, then directly compares the face data, and transmits a comparison result to the cloud control deployment server through the directory server.
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| CN202010055048.8A CN111242067A (en) | 2020-01-17 | 2020-01-17 | Face recognition system |
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| CN202010055048.8A CN111242067A (en) | 2020-01-17 | 2020-01-17 | Face recognition system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113160444A (en) * | 2021-04-20 | 2021-07-23 | 范传进 | Attendance machine based on end pipe cloud architecture and use method thereof |
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| US20180068173A1 (en) * | 2016-09-02 | 2018-03-08 | VeriHelp, Inc. | Identity verification via validated facial recognition and graph database |
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