CN111652053A - Employee attendance checking method, device and medium - Google Patents
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Abstract
The application discloses a method, equipment and medium for attendance checking of staff, comprising the following steps: inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer; recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme. The embodiment of the application inputs the acquired staff image into the staff attendance model, and the staff is subjected to attendance record, so that the staff can be well prevented from forgetting to sign in and sign off, an attendance state can be generated according to the attendance record of the staff, and the staff can conveniently count attendance statistics.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, and a medium for attendance checking by an employee.
Background
At present, the staff of business office signs in and signs out is the normal demand of the company, but because the traditional mode of signing in and signing out needs the staff to carry out manually, some staff forget to sign in and sign out, therefore, in the prior art, a technology of signing in which can be fast, convenient and accurate is needed, and the attendance state of the staff is generated according to the signing in content.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, an apparatus, and a medium for attendance checking of employees, which are used to solve the problems mentioned in the background art.
The embodiment of the application adopts the following technical scheme:
the embodiment of the application provides an employee attendance checking method, which comprises the following steps:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
Further, before the collected employee image is input to the pre-trained employee attendance model, the method further includes:
acquiring a sample set, wherein the sample set comprises head portrait pictures of all employees;
marking the sample set according to the information of the staff;
establishing an initial staff attendance model;
and training the initial staff attendance model according to the marked sample set to obtain a staff attendance model meeting preset conditions.
Further, after the obtaining the sample set, the method further comprises:
the method comprises the steps of carrying out noise reduction, binarization, character segmentation and zero homogenization treatment on head portrait pictures of all employees, and inputting the treated head portrait pictures of all employees into an image processor so as to obtain the head portrait pictures of all employees with preset sizes.
Further, training an initial staff attendance model according to the marked sample set to obtain a staff attendance model meeting preset conditions, and specifically comprising:
dividing the marked sample set into a training test set and a verification set according to a preset proportion;
verifying and dividing a training test set according to a ten-fold cross-verification team, dividing the training test set with a first preset proportion into test sets, and dividing the training test set with a second preset proportion into training sets
Training an initial employee attendance model according to the training set and the test set;
screening a first employee attendance model trained through the training set and the testing set according to a voting method, inputting the verification set into the first employee attendance model, and determining a cost value of the first employee attendance model according to a cost function;
and if the cost value is in a preset threshold value, the first staff attendance model is a staff attendance model meeting the conditions.
Further, the cost function is a normalized exponential function.
Furthermore, the initial staff attendance model comprises a plurality of convolutional layers, a pooling layer and a full-connection layer.
Further, the initial employee attendance model specifically includes:
the first layer is an image input layer;
a second layer comprising a convolutional layer of 7x7 convolutional kernels and a pooling layer of 3x3 pooling kernels;
a third layer comprising convolutional layers of 3x3 convolutional kernels and 3x3 pooling kernels;
a fourth layer comprising four branches, respectively: a convolutional layer of 64 convolutional kernels of 1 × 1; 96 convolutional layers of 1 × 1 convolutional kernels and 128 convolutional layers of 3 × 3 convolutional kernels; 16 convolutional layers consisting of 1 × 1 convolutional kernels and 32 convolutional layers consisting of 5 × 5 convolutional kernels; convolutional layers consisting of convolution kernels of 3x3 and 32 convolution layers consisting of convolution kernels of 1x 1;
and a fifth layer comprising a pooling layer of 7x7x1024 pooling cores.
Further, after the initial employee attendance model is established, the method further includes:
and optimizing the initial employee attendance model according to batch-normal, prelu activation function or dropout.
An embodiment of the present application further provides an employee attendance device, the device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
The embodiment of the application also provides an employee attendance medium, which stores computer executable instructions, wherein the computer executable instructions are set as follows:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: the embodiment of the application inputs the acquired staff image into the staff attendance model, and the staff is subjected to attendance record, so that the staff can be well prevented from forgetting to sign in and sign off, an attendance state can be generated according to the attendance record of the staff, and the staff can conveniently count attendance statistics.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of an employee attendance method provided in an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of an employee attendance checking method provided in the second embodiment of the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an employee attendance method provided in an embodiment of the present specification, where the embodiment of the present specification may be implemented by an employee attendance system, and the method specifically includes the following steps:
step S101, the staff attendance system inputs the collected staff images to a staff attendance model trained in advance to determine the staff information, wherein the staff identification information comprises the staff identification, the department to which the staff belongs and the staff position information, and the staff attendance model comprises a convolutional layer, a pooling layer and a full connection layer.
And S102, the staff attendance system records the attendance time or the attendance time of the staff according to the identification of the staff and determines the attendance state of the staff according to a preset attendance scheme.
Corresponding to the first embodiment, fig. 2 is a schematic flow chart of an employee attendance method provided in the second embodiment of the present specification, and the second embodiment of the present specification may be implemented by an employee attendance system, which specifically includes the following steps:
step S201, the staff attendance system obtains a sample set, wherein the sample set comprises head portrait pictures of all staff.
In step S201 of the embodiment of the present specification, after this step, the method further includes:
the method comprises the steps of carrying out noise reduction, binarization, character segmentation and zero homogenization treatment on head portrait pictures of all employees, and inputting the treated head portrait pictures of all employees into an image processor so as to obtain the head portrait pictures of all employees with preset sizes. The preset size may be 224x224x 3.
And step S202, the staff attendance system marks the sample set according to the information of the staff.
Head portrait pictures of employees of a company are collected and marked, so that personal information of the employee to which each head portrait picture belongs can be marked, and the personal information can be the number of the employee.
Step S203, the staff attendance system establishes an initial staff attendance model.
The initial employee attendance model comprises a plurality of convolutional layers, a pooling layer and a full-link layer.
The initial employee attendance model may specifically include:
the first layer is an image input layer;
a second layer comprising a convolutional layer of 7x7 convolutional kernels and a pooling layer of 3x3 pooling kernels;
a third layer comprising convolutional layers of 3x3 convolutional kernels and 3x3 pooling kernels;
a fourth layer comprising four branches, respectively: a convolutional layer of 64 convolutional kernels of 1 × 1; 96 convolutional layers of 1 × 1 convolutional kernels and 128 convolutional layers of 3 × 3 convolutional kernels; 16 convolutional layers consisting of 1 × 1 convolutional kernels and 32 convolutional layers consisting of 5 × 5 convolutional kernels; convolutional layers consisting of convolution kernels of 3x3 and 32 convolution layers consisting of convolution kernels of 1x 1;
and a fifth layer comprising a pooling layer of 7x7x1024 pooling cores.
Further, the initial staff attendance model specifically includes when training:
the first layer is a feature map input layer, that is, the feature map that can be input is 224x224x3, and the zero equalization processing is performed before the feature map is input;
a second layer, which may use a convolution kernel of 7 × 7, where the step size of sliding is 2, padding (padding) is 3 and 64 channels, the output feature map is 112 × 64, a Linear rectification function (ReLU) is performed after convolution, and then a pooling kernel of 3 × 3 (max boosting) (step size is 2), and the output feature map is ((112-3+1)/2) +1 ═ 56, that is, a feature map of 56 × 64, and then the operation of the Linear rectification function is performed;
a third layer, a convolution kernel of 3 × 3 may be used, where the step size of sliding is 1, the filling is 1 and 192 channels, the output feature map is 56 × 192, a linear rectification function operation is performed after convolution, and then a pooling kernel of 3x3 is performed (the step size is 2), the output feature map is ((56-3+1)/2) +1 ═ 28, that is, a feature map of 28 × 192, and then the operation of the linear rectification function is performed;
and the fourth layer is divided into four branches, and convolution kernels with different scales can be adopted for processing:
(1) a feature map of 28x28x64 can be output by performing a convolution operation using 64 convolution kernels of 1x1, followed by a linear rectification function;
(2) 96 convolution kernels of 1x1 can be used for convolution operation to output a characteristic map of 28x28x96, then operation of linear rectification function is carried out, and then convolution operation of 128 convolution kernels of 3x3 (filled with 1) is carried out to output a characteristic map of 28x28x 128;
(3) 16 convolution kernels of 1x1 can be used for convolution operation to output a characteristic map of 28x28x16, then operation of linear rectification function is carried out, 32 convolution kernels of 5x5 (filled with 2) are carried out, and a characteristic map of 28x28x32 is output;
(4) a convolution operation can be performed by using a convolution kernel (padding is 1) of 3x3 to output a 28x28x192 characteristic diagram, and then 32 convolution kernels of 1x1 are performed to perform the convolution operation to output a 28x28x32 characteristic diagram;
connecting the four results, and connecting the three dimensions of the four output results in parallel, namely, 256 is 64+128+32+32, and finally, a 28x28x256 feature map can be output. The following operations may then be performed on the feature map:
(1) performing a convolution operation by using 128 convolution kernels of 1x1 and then performing an operation of a linear rectification function to output a 28x28x128 characteristic diagram;
(2) performing convolution operation by using 128 convolution kernels of 1x1, changing the convolution operation into a characteristic map of 28x28x128 as dimensionality reduction before a convolution kernel of 3x3, then performing operation of a linear rectification function, performing convolution operation of 192 convolution kernels of 3x3 (padding is 1), and outputting a characteristic map of 28x28x 192;
(3) performing convolution operation by using 32 convolution kernels of 1x1, changing the convolution kernels into a characteristic map of 28x28x32 as dimensionality reduction before a convolution kernel of 5x5, then performing operation of a linear rectification function, further performing convolution operation of 96 convolution kernels of 5x5 (padding is 2), and outputting the characteristic map of 28x28x 96;
(4) the convolution operation is performed by using a convolution kernel (padding is 1) of 3x3, a feature map of 28x28x256 is output, and then the convolution operation of 64 convolution kernels of 1x1 is performed, and a feature map of 28x28x64 is output.
And connecting the four results, and connecting the three dimensions of the four output results in parallel, namely 128+192+96+ 64-480, and finally outputting a 28x28x480 feature map.
The fourth layer structure and the fifth layer structure are the same as the third layer structure, and finally output a 7x7x1024 characteristic diagram.
Sixth, a 1x1x1000 profile can be obtained using an average pooling of 7x7x 1024.
Further, after the initial employee attendance model is established, the method further comprises:
and optimizing the initial staff attendance model according to the batch-normal, prelu activation function or dropout so as to prevent problems of overfitting, gradient disappearance, gradient explosion and the like.
And step S204, the staff attendance system trains the initial staff attendance model according to the marked sample set to obtain a staff attendance model meeting preset conditions.
Training an initial staff attendance model according to the marked sample set to obtain a staff attendance model meeting preset conditions, and specifically comprising the following steps of:
dividing the marked sample set into a training test set and a verification set according to a preset proportion, wherein the training set can be divided into the training test set and the verification set according to the ratio of 1: 5;
verifying and dividing a training test set according to a ten-fold cross-validation team, dividing the training test set with a first preset proportion into test sets, and dividing the training test set with a second preset proportion into training sets, wherein for example, one tenth of the training test sets can be used as a test set, and the rest nine tenths can be used as training sets;
training an initial employee attendance model according to the training set and the test set;
screening a first employee attendance model trained through the training set and the testing set according to a voting method, inputting the verification set into the first employee attendance model, and determining a cost value of the first employee attendance model according to a cost function;
and if the cost value is in a preset threshold value, the first staff attendance model is a staff attendance model meeting the conditions.
It should be noted that the cost function is a normalized exponential function.
Step S205, the staff attendance system inputs the collected staff images to a staff attendance model trained in advance to determine the staff information, wherein the staff identification information comprises the staff identification, the department to which the staff belongs and the staff position information, and the staff attendance model comprises a convolutional layer, a pooling layer and a full connection layer.
And step S206, the staff attendance system records the attendance time or the attendance time of the staff according to the identification of the staff and determines the attendance state of the staff according to a preset attendance scheme.
It should be noted that, the opencv may be used to read an employee image, preprocess the employee image, input the preprocessed employee image into a pre-trained employee attendance model, obtain information of the employee, record the attendance time or attendance time of the employee, and determine the attendance state of the employee according to a preset attendance scheme. Opencv can process the employee images into a format with a unified specification so that an employee attendance model can process the employee images. The attendance scheme can be preset check-in time and check-out time.
An embodiment of the present application further provides an employee attendance device, the device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
The embodiment of the application also provides an employee attendance medium, which stores computer executable instructions, wherein the computer executable instructions are set as follows:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. An employee attendance method, characterized in that the method comprises:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
2. The employee attendance method of claim 1 wherein prior to inputting the captured employee image to a pre-trained employee attendance model, the method further comprises:
acquiring a sample set, wherein the sample set comprises head portrait pictures of all employees;
marking the sample set according to the information of the staff;
establishing an initial staff attendance model;
and training the initial staff attendance model according to the marked sample set to obtain a staff attendance model meeting preset conditions.
3. The employee attendance method of claim 2 wherein after the obtaining of the sample set, the method further comprises:
the method comprises the steps of carrying out noise reduction, binarization, character segmentation and zero homogenization treatment on head portrait pictures of all employees, and inputting the treated head portrait pictures of all employees into an image processor so as to obtain the head portrait pictures of all employees with preset sizes.
4. The employee attendance method of claim 2 wherein the initial employee attendance model comprises a plurality of convolutional layers, pooled layers, and fully-connected layers.
5. The employee attendance method according to claim 4, wherein the initial employee attendance model specifically comprises:
the first layer is an image input layer;
a second layer comprising a convolutional layer of 7x7 convolutional kernels and a pooling layer of 3x3 pooling kernels;
a third layer comprising convolutional layers of 3x3 convolutional kernels and 3x3 pooling kernels;
a fourth layer comprising four branches, respectively: a convolutional layer of 64 convolutional kernels of 1 × 1; 96 convolutional layers of 1 × 1 convolutional kernels and 128 convolutional layers of 3 × 3 convolutional kernels; 16 convolutional layers consisting of 1 × 1 convolutional kernels and 32 convolutional layers consisting of 5 × 5 convolutional kernels; convolutional layers consisting of convolution kernels of 3x3 and 32 convolution layers consisting of convolution kernels of 1x 1;
and a fifth layer comprising a pooling layer of 7x7x1024 pooling cores.
6. The employee attendance method of claim 2 wherein after the establishing of the initial employee attendance model, the method further comprises:
and optimizing the initial employee attendance model according to batch-normal, prelu activation function or dropout.
7. An employee attendance device, the device comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
8. An employee attendance medium having stored thereon computer-executable instructions configured to:
inputting the collected employee image to a pre-trained employee attendance model to determine information of the employee, wherein the identification information of the employee comprises an identification of the employee, a department to which the employee belongs and position information of the employee, and the employee attendance model comprises a convolutional layer, a pooling layer and a full-link layer;
recording the sign-in time or sign-out time of the employee according to the identification of the employee, and determining the attendance state of the employee according to a preset attendance scheme.
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