CN111160148A - Teaching attendance identification system and method based on pixel difference - Google Patents
Teaching attendance identification system and method based on pixel difference Download PDFInfo
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- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
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Abstract
The invention discloses a teaching attendance recognition system and a method based on pixel difference, which relate to the technical field of face image recognition, and the system comprises: the system comprises a face image acquisition device, a face image preprocessing device, a face image recognition device and a user side; the face image acquisition device is used for acquiring face images of students in a classroom; the face image preprocessing device is used for preprocessing the face image of the collected face image; the face image recognition device is used for carrying out face image recognition on the face image subjected to face image preprocessing, calculating the attendance and absence personnel and sending the calculation result; the user side is used for receiving and displaying the calculation result sent by the face image recognition device; the human face image acquisition device is in signal connection with the human face image preprocessing device; the human face image preprocessing device is connected with the human face image recognition device through signals; the face image recognition device is connected with the user side through signals. The method has the advantages of accurate identification and high intelligent degree.
Description
Technical Field
The invention relates to the technical field of face image recognition, in particular to a teaching attendance recognition system and method based on pixel difference.
Background
Attendance is indispensable ー works of present company and enterprise, and at present, the task is mainly accomplished to there being attendance system, and general attendance system all includes discernment staff identity client and handles the server side of attendance information. At present, all commonly used clients are special equipment, the price of the special equipment is expensive, a company generally cannot be provided with a plurality of clients for staff to use, and at present, almost no non-matched professional equipment can complete the work of the attendance system client. Under the condition, when thousands of employees of a large-scale enterprise come on duty, the input end of the limited attendance system of the enterprise bears a difficult task, no error can be caused, and the attendance information input of the people without attendance can be directly influenced when a problem occurs.
Different defects also exist in different clients at present, for example, the common attendance punched-card machine has the possibility of punching cards by other people; the fingerprint and face recognition has the possibility of failure of identity recognition; part of mobile terminal attendance systems capable of identifying identities have the possibility of overhigh attendance cost. The above attendance checking modes have space limitation, and one-time attendance checking can be completed only by passing through the position of the attendance checking client.
Nowadays, more and more people habitually draw out mobile phones when shopping, dining, going on a journey and working, check information, find positions, share feelings, … … hundreds of millions of users log in the mobile internet in cooperation with work, stay on the mobile internet for tens of minutes or even more than ten hours, and live, work, trade and make friends on the mobile internet. It can be known that the mobile internet is in the vogue of most office workers, and mobile internet terminals such as smart phones and tablet computers have entered the lives of people. And hardware modules such as a camera and a GPS are carried, which is the basic requirement of the mobile internet terminal.
The existing attendance system usually needs manual recording or adopts a card punching system, so that the accuracy of attendance cannot be realized.
Disclosure of Invention
In view of this, the invention aims to provide a teaching attendance recognition system and method based on pixel difference, which have the advantages of accurate recognition and high intelligent degree.
In order to achieve the purpose, the invention adopts the following technical scheme:
a pixel difference-based teaching attendance identification system, the system comprising: the system comprises a face image acquisition device, a face image preprocessing device, a face image recognition device and a user side; the face image acquisition device is used for acquiring face images of students in the classroom; the face image preprocessing device is used for preprocessing the face image of the collected face image; the face image recognition device is used for carrying out face image recognition on the face image after the face image preprocessing, calculating the attendance and absence personnel and sending the calculation result; the user side is used for receiving and displaying the calculation result sent by the face image recognition device; the human face image acquisition device is in signal connection with the human face image preprocessing device; the human face image preprocessing device is connected with the human face image recognition device through signals; the face image recognition device is connected with the user side through signals.
Further, the face image recognition apparatus includes: the identification unit is used for identifying the face image sample by adopting a face image classification model to obtain the face image class confidence of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples when the confidence coefficient of the face image category obtained by the identification unit is smaller than a first preset threshold value, and taking the identification result of the target artificial identification sample with the highest confidence coefficient in the similar artificial identification samples as the identification result of the face image sample; the training unit is used for training the face image classification model according to the training samples in the training sample library; the training samples comprise manual identification samples and high-confidence face image retrieval samples; the high-confidence face image retrieval sample is a manual identification sample retrieved by the retrieval unit.
Further, when the confidence of the face image category of the face image sample is greater than or equal to the first predetermined threshold, the retrieval unit stores the face image sample as a high-confidence face image retrieval sample into a training sample library.
Further, the identification unit is further configured to obtain a face image feature of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples from a face image sample library according to the face image characteristics of the face image samples obtained by the identification unit; the similarity between the facial image features contained in the recognition result of the similar artificial recognition sample and the facial image features of the facial image sample is greater than a second preset threshold value; wherein, the face image sample library is used for storing one or more artificial identification samples.
Further, the face image preprocessing device comprises: the function constructing unit is used for selecting the window size of filtering and the smoothness parameter of filtering to construct a filtering function; the calculating unit is used for respectively calculating first-order partial derivatives of the filter function in different directions; and the filtering unit is used for performing convolution operation on the first-order partial derivatives in different directions, the original image in the corresponding direction and the image to be registered in the corresponding direction respectively to obtain the image to be registered after filtering in the corresponding direction.
A teaching attendance identification method based on pixel difference executes the following steps:
step 1: the face image acquisition device is used for acquiring face images of students in the classroom;
step 2: the human face image preprocessing device is used for preprocessing the human face image of the collected human face image;
and step 3: the face image recognition device is used for carrying out face image recognition on the face image after the face image preprocessing, calculating the attendance and absence personnel and sending the calculation result;
and 4, step 4: and the user side receives and displays the calculation result sent by the face image recognition device.
Further, the face image recognition apparatus includes: the identification unit is used for identifying the face image sample by adopting a face image classification model to obtain the face image class confidence of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples when the confidence coefficient of the face image category obtained by the identification unit is smaller than a first preset threshold value, and taking the identification result of the target artificial identification sample with the highest confidence coefficient in the similar artificial identification samples as the identification result of the face image sample; the training unit is used for training the face image classification model according to the training samples in the training sample library; the training samples comprise manual identification samples and high-confidence face image retrieval samples; the high-confidence face image retrieval sample is a manual identification sample retrieved by the retrieval unit.
Further, the identification unit is further configured to obtain a face image feature of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples from a face image sample library according to the face image characteristics of the face image samples obtained by the identification unit; the similarity between the facial image features contained in the recognition result of the similar artificial recognition sample and the facial image features of the facial image sample is greater than a second preset threshold value; wherein, the face image sample library is used for storing one or more artificial identification samples.
Further, the face image preprocessing device comprises: the function constructing unit is used for selecting the window size of filtering and the smoothness parameter of filtering to construct a filtering function; the calculating unit is used for respectively calculating first-order partial derivatives of the filter function in different directions; and the filtering unit is used for performing convolution operation on the first-order partial derivatives in different directions, the original image in the corresponding direction and the image to be registered in the corresponding direction respectively to obtain the image to be registered after filtering in the corresponding direction.
Compared with the prior art, the invention has the following beneficial effects: the attendance system and the method thereof have the advantages of easy use for users, no need of other professional attendance clients, no need of identification equipment for identity verification, no user cheating behavior and the like.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
fig. 1 is a schematic structural diagram of a teaching attendance identification system based on pixel difference disclosed in the embodiment of the present invention.
Fig. 2 is a schematic flow chart of a teaching attendance identification method based on pixel difference disclosed by the embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Example 1
A pixel difference-based teaching attendance identification system, the system comprising: the system comprises a face image acquisition device, a face image preprocessing device, a face image recognition device and a user side; the face image acquisition device is used for acquiring face images of students in the classroom; the face image preprocessing device is used for preprocessing the face image of the collected face image; the face image recognition device is used for carrying out face image recognition on the face image after the face image preprocessing, calculating the attendance and absence personnel and sending the calculation result; the user side is used for receiving and displaying the calculation result sent by the face image recognition device; the human face image acquisition device is in signal connection with the human face image preprocessing device; the human face image preprocessing device is connected with the human face image recognition device through signals; the face image recognition device is connected with the user side through signals.
Specifically, the face recognition refers to a computer technology for identifying an identity by analyzing and comparing visual characteristic information of a face. The generalized face recognition actually comprises a series of related technologies for constructing a face recognition system, including face image acquisition, face positioning, face recognition preprocessing, identity confirmation, identity search and the like; the narrow-sense face recognition refers to a technique or system for identity confirmation or identity search through a face. Human face recognition is a popular research field of computer technology, belongs to the technology of biological feature recognition, and is used for distinguishing organism individuals from biological features of organisms (generally, specially, people). The biological characteristics studied by the biological characteristic recognition technology include face, fingerprint, palm print, iris, retina, voice, body shape, personal habits (for example, strength, frequency and signature of keyboard knocking), and the like, and the corresponding recognition technologies include face recognition, fingerprint recognition, palm print recognition, iris recognition, retina recognition, voice recognition (identity recognition can be performed by voice recognition, and also voice content recognition can be performed, and only the former belongs to the biological characteristic recognition technology), body shape recognition, keyboard knocking recognition, signature recognition, and the like.
Example 2
On the basis of the above embodiment, the face image recognition apparatus includes: the identification unit is used for identifying the face image sample by adopting a face image classification model to obtain the face image class confidence of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples when the confidence coefficient of the face image category obtained by the identification unit is smaller than a first preset threshold value, and taking the identification result of the target artificial identification sample with the highest confidence coefficient in the similar artificial identification samples as the identification result of the face image sample; the training unit is used for training the face image classification model according to the training samples in the training sample library; the training samples comprise manual identification samples and high-confidence face image retrieval samples; the high-confidence face image retrieval sample is a manual identification sample retrieved by the retrieval unit.
Example 3
On the basis of the previous embodiment, when the confidence of the face image category of the face image sample is greater than or equal to the first predetermined threshold, the retrieval unit stores the face image sample as a high-confidence face image retrieval sample into a training sample library.
Example 4
On the basis of the previous embodiment, the identification unit is further configured to obtain a face image feature of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples from a face image sample library according to the face image characteristics of the face image samples obtained by the identification unit; the similarity between the facial image features contained in the recognition result of the similar artificial recognition sample and the facial image features of the facial image sample is greater than a second preset threshold value; wherein, the face image sample library is used for storing one or more artificial identification samples.
Example 5
On the basis of the previous embodiment, the face image preprocessing device comprises: the function constructing unit is used for selecting the window size of filtering and the smoothness parameter of filtering to construct a filtering function; the calculating unit is used for respectively calculating first-order partial derivatives of the filter function in different directions; and the filtering unit is used for performing convolution operation on the first-order partial derivatives in different directions, the original image in the corresponding direction and the image to be registered in the corresponding direction respectively to obtain the image to be registered after filtering in the corresponding direction.
Example 6
As shown in fig. 2, a teaching attendance recognition method based on pixel difference performs the following steps:
step 1: the face image acquisition device is used for acquiring face images of students in the classroom;
step 2: the human face image preprocessing device is used for preprocessing the human face image of the collected human face image;
and step 3: the face image recognition device is used for carrying out face image recognition on the face image after the face image preprocessing, calculating the attendance and absence personnel and sending the calculation result;
and 4, step 4: and the user side receives and displays the calculation result sent by the face image recognition device.
Specifically, the development of image recognition goes through three stages: character recognition, digital image processing and recognition, and object recognition. The research on character recognition started in 1950, generally recognizing letters, numbers and symbols, and has been widely used from printed character recognition to handwritten character recognition.
Research into digital image processing and recognition began in 1965. Compared with analog images, the digital images have the great advantages of storage, convenience and compressibility in transmission, difficulty in distortion in the transmission process, convenience in processing and the like, and the digital images provide strong power for the development of image recognition technology. The recognition of objects mainly refers to the perception and understanding of objects and environments in the three-dimensional world, and belongs to the field of advanced computer vision. The method is a research direction combining subjects such as artificial intelligence, systematics and the like based on digital image processing and recognition, and research results of the method are widely applied to various industries and detection robots. One of the disadvantages of the modern image recognition technology is poor self-adaption performance, and once a target image is polluted by strong noise or has large defects, an ideal result cannot be obtained.
The mathematical nature of the image recognition problem pertains to the mapping problem of the pattern space to the class space. Currently, in the development of image recognition, there are mainly three recognition methods: statistical pattern recognition, structural pattern recognition, fuzzy pattern recognition. Image segmentation is a key technology in image processing, and since the 70's of the 20 th century, research on the image segmentation has been in history for decades and has been highly valued by people, thousands of segmentation algorithms are proposed by means of various theories so far, and research on the aspect is still actively carried out.
There are many methods for image segmentation, including a threshold segmentation method, an edge detection method, a region extraction method, a segmentation method combined with a specific theoretical tool, and the like. From the type of image, there are: grayscale image segmentation, color image segmentation, texture image segmentation, and the like. The detection of edge operators was proposed as early as 1965, so that edge detection produced many classical algorithms. However, in the last two decades, with the rapid development of the computational and VLSI techniques for image segmentation based on histogram and wavelet transform, the research on image processing has been greatly advanced. The image segmentation method incorporates some specific theories, methods and tools, such as image segmentation based on mathematical morphology, segmentation based on wavelet transform, segmentation based on genetic algorithm, etc.
Example 7
On the basis of the above embodiment, the face image recognition apparatus includes: the identification unit is used for identifying the face image sample by adopting a face image classification model to obtain the face image class confidence of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples when the confidence coefficient of the face image category obtained by the identification unit is smaller than a first preset threshold value, and taking the identification result of the target artificial identification sample with the highest confidence coefficient in the similar artificial identification samples as the identification result of the face image sample; the training unit is used for training the face image classification model according to the training samples in the training sample library; the training samples comprise manual identification samples and high-confidence face image retrieval samples; the high-confidence face image retrieval sample is a manual identification sample retrieved by the retrieval unit.
Example 8
On the basis of the previous embodiment, the identification unit is further configured to obtain a face image feature of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples from a face image sample library according to the face image characteristics of the face image samples obtained by the identification unit; the similarity between the facial image features contained in the recognition result of the similar artificial recognition sample and the facial image features of the facial image sample is greater than a second preset threshold value; wherein, the face image sample library is used for storing one or more artificial identification samples.
Example 9
On the basis of the previous embodiment, the face image preprocessing device comprises: the function constructing unit is used for selecting the window size of filtering and the smoothness parameter of filtering to construct a filtering function; the calculating unit is used for respectively calculating first-order partial derivatives of the filter function in different directions; and the filtering unit is used for performing convolution operation on the first-order partial derivatives in different directions, the original image in the corresponding direction and the image to be registered in the corresponding direction respectively to obtain the image to be registered after filtering in the corresponding direction.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or part of the functions described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the software elements, method steps, and corresponding programs may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (9)
1. A teaching attendance identification system based on pixel differences, the system comprising: the system comprises a face image acquisition device, a face image preprocessing device, a face image recognition device and a user side; the face image acquisition device is used for acquiring face images of students in the classroom; the face image preprocessing device is used for preprocessing the face image of the collected face image; the face image recognition device is used for carrying out face image recognition on the face image after the face image preprocessing, calculating the attendance and absence personnel and sending the calculation result; the user side is used for receiving and displaying the calculation result sent by the face image recognition device; the human face image acquisition device is in signal connection with the human face image preprocessing device; the human face image preprocessing device is connected with the human face image recognition device through signals; the face image recognition device is connected with the user side through signals.
2. The system of claim 1, wherein the facial image recognition device comprises: the identification unit is used for identifying the face image sample by adopting a face image classification model to obtain the face image class confidence of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples when the confidence coefficient of the face image category obtained by the identification unit is smaller than a first preset threshold value, and taking the identification result of the target artificial identification sample with the highest confidence coefficient in the similar artificial identification samples as the identification result of the face image sample; the training unit is used for training the face image classification model according to the training samples in the training sample library; the training samples comprise manual identification samples and high-confidence face image retrieval samples; the high-confidence face image retrieval sample is a manual identification sample retrieved by the retrieval unit.
3. The system according to claim 2, wherein the retrieval unit stores the face image sample as a high-confidence face image retrieval sample into a training sample library when the face image class confidence of the face image sample is greater than or equal to the first predetermined threshold.
4. The system of claim 3, wherein the recognition unit is further configured to obtain facial image features of the facial image samples; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples from a face image sample library according to the face image characteristics of the face image samples obtained by the identification unit; the similarity between the facial image features contained in the recognition result of the similar artificial recognition sample and the facial image features of the facial image sample is greater than a second preset threshold value; wherein, the face image sample library is used for storing one or more artificial identification samples.
5. The system of claim 4, wherein the face image preprocessing means comprises: the function constructing unit is used for selecting the window size of filtering and the smoothness parameter of filtering to construct a filtering function; the calculating unit is used for respectively calculating first-order partial derivatives of the filter function in different directions; and the filtering unit is used for performing convolution operation on the first-order partial derivatives in different directions, the original image in the corresponding direction and the image to be registered in the corresponding direction respectively to obtain the image to be registered after filtering in the corresponding direction.
6. A pixel difference-based teaching attendance identification method based on the system of any one of claims 1 to 5, characterized in that the method performs the following steps:
step 1: the face image acquisition device is used for acquiring face images of students in the classroom;
step 2: the human face image preprocessing device is used for preprocessing the human face image of the collected human face image;
and step 3: the face image recognition device is used for carrying out face image recognition on the face image after the face image preprocessing, calculating the attendance and absence personnel and sending the calculation result;
and 4, step 4: and the user side receives and displays the calculation result sent by the face image recognition device.
7. The method of claim 6, wherein the face image recognition device comprises: the identification unit is used for identifying the face image sample by adopting a face image classification model to obtain the face image class confidence of the face image sample; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples when the confidence coefficient of the face image category obtained by the identification unit is smaller than a first preset threshold value, and taking the identification result of the target artificial identification sample with the highest confidence coefficient in the similar artificial identification samples as the identification result of the face image sample; the training unit is used for training the face image classification model according to the training samples in the training sample library; the training samples comprise manual identification samples and high-confidence face image retrieval samples; the high-confidence face image retrieval sample is a manual identification sample retrieved by the retrieval unit.
8. The method of claim 7, wherein the recognition unit is further configured to obtain facial image features of the facial image samples; the retrieval unit is used for retrieving similar artificial identification samples of the face image samples from a face image sample library according to the face image characteristics of the face image samples obtained by the identification unit; the similarity between the facial image features contained in the recognition result of the similar artificial recognition sample and the facial image features of the facial image sample is greater than a second preset threshold value; wherein, the face image sample library is used for storing one or more artificial identification samples.
9. The method of claim 8, wherein the face image preprocessing means comprises: the function constructing unit is used for selecting the window size of filtering and the smoothness parameter of filtering to construct a filtering function; the calculating unit is used for respectively calculating first-order partial derivatives of the filter function in different directions; and the filtering unit is used for performing convolution operation on the first-order partial derivatives in different directions, the original image in the corresponding direction and the image to be registered in the corresponding direction respectively to obtain the image to be registered after filtering in the corresponding direction.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040247168A1 (en) * | 2000-06-05 | 2004-12-09 | Pintsov David A. | System and method for automatic selection of templates for image-based fraud detection |
| CN105761236A (en) * | 2015-11-16 | 2016-07-13 | 乐视致新电子科技（天津）有限公司 | Image preprocessing method and apparatus for image registering |
| CN107690659A (en) * | 2016-12-27 | 2018-02-13 | 深圳前海达闼云端智能科技有限公司 | Image recognition system and image recognition method |
| CN110097003A (en) * | 2019-04-29 | 2019-08-06 | 中南民族大学 | Check class attendance method, equipment, storage medium and device neural network based |
| CN110135505A (en) * | 2019-05-20 | 2019-08-16 | 北京达佳互联信息技术有限公司 | Image classification method, device, computer equipment and computer readable storage medium |
-
2019
- 2019-12-16 CN CN201911294935.4A patent/CN111160148A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040247168A1 (en) * | 2000-06-05 | 2004-12-09 | Pintsov David A. | System and method for automatic selection of templates for image-based fraud detection |
| CN105761236A (en) * | 2015-11-16 | 2016-07-13 | 乐视致新电子科技（天津）有限公司 | Image preprocessing method and apparatus for image registering |
| CN107690659A (en) * | 2016-12-27 | 2018-02-13 | 深圳前海达闼云端智能科技有限公司 | Image recognition system and image recognition method |
| CN110097003A (en) * | 2019-04-29 | 2019-08-06 | 中南民族大学 | Check class attendance method, equipment, storage medium and device neural network based |
| CN110135505A (en) * | 2019-05-20 | 2019-08-16 | 北京达佳互联信息技术有限公司 | Image classification method, device, computer equipment and computer readable storage medium |
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Application publication date: 20200515 |