This invention relates to automated attendance system for multiple students using neural network.
BACKGROUND OF THE INVENTION
Maintaining attendance is an important component of ensuring that a student is present in a school or institution in today's world. There are numerous methods in use today that take attendance in various ways. The traditional method of taking attendance is a manual process that necessitates human involvement. In most cases, one period lasts 60 to 50 minutes, with 10 to 15 minutes devoted to taking attendance. This old procedure takes a long time.
After considering the problem, we propose a CNN based attendance system. With the help of this system, we capture multiple objects or entities within the timeline and verify the whole attendance in a systematic way for tracking student attendance
CN104915999A Disclosed is a schoolyard remote attendance system. The schoolyard remote attendance system comprises RFID (radio frequency identification) student attendance cards, low frequency triggers, an RFID reader, a school data center and a client side, wherein the low frequency triggers are connected with the RFID reader, the low frequency triggers are arranged on the inner side and the outer side of a school gate, the RFID student attendance cards can trigger the low frequency triggers in a low frequency trigger area, the RFID reader is connected with the school data center through a mobile private network, and the client side is connected with the school data center through the mobile private network. The schoolyard remote attendance system does not need students to swipe the RFID student attendance cards, and only needs the students carrying the RFID student attendance cards to enter an induction area so as to read the RFID student attendance cards and check attendance. Compared with traditional card swiping attendance checking, the schoolyard remote attendance system can enable the number of the students who simultaneously enter and exit from the school gate to reach 500 through remote attendance checking, enables card leakage rate to reach 1%, and has the advantages of preventing the students from drawing out the RFID student attendance cards and being high in attendance checking efficiency. The major difference is to identify the students using RFID but we used CNN based edge assisted vision devices.
CN106204779A discloses a kind of check class attendance method learnt based on plurality of human faces data collection strategy and the degree of depth, for solving the technical problem of existing Work attendance method identification rate variance based on recognition of face. Technical scheme is to utilize AdaBoost algorithm and complexion model to carry out multi-target detection and extraction. Only once the face of all participation work attendances need to be shot one section of video, and the face in video sequence is detected, extracts, complete the foundation of face database. Face identification method based on degree of depth study, based on degree of depth convolutional neural networks LeNet 5 model, the face characteristic under scenes different in face database is learnt by LeNet 5 model that application simplifies, and obtains new feature by multilayered nonlinear conversion and represents. These new features are as much as possible to be eliminated as changed in the classes such as illumination, noise, attitude and expression, and retains and change between the class that identity difference produces, and improves face identification method at actual complex scene human face discrimination. The main advantage is to cover the features identification as well as the face identification.
US20060069576A1 relates to the method for a prospective college student college search and recommendation system. The invention is generally including a method of receiving survey data gathered from a prospective college student, analyzing the survey data for relationships between one or more variables which correlate with actual college student satisfaction with their college experience; and identifying one or more candidate colleges for the prospective college student to consider by determining an association between the prospective college student's survey data and candidate colleges.
Research Gap: Only survey is analyzed but we captured real time objects or identify the student’s entry during the attendance system.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is Automated Attendance System for Multiple Students using Neural Network.
` SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
Discloses herein an automated attendance system for multiple students using neural network comprises Edge Assisted Vision Device (100), Control Unit (200), Power Supply (201), Neural Network (202), RTC (203), Camera (204), GPS (205), Storage (206), Local Server (101); and Cloud Server (102) , wherein the system deals with the detection of the multiple objects (students) and local server and cloud server are connected to the edge device to send the notification of attendance system.
In another embodiment, Control Unit makes a sequence of all modules related to edge assisted vision device.
In another embodiment, Power Supply (201) is the main energy source where it deals with the power generation and the current is passed over the system.
In another embodiment, Neural Network (202) captures the biological feature and convert into the data; and said RTC (203) provides the real time communication over the network.
In another embodiment, Camera (204) captures the multiple objects which is inbuilt in the edge device; and GPS (205) manages the location of the objects.
In another embodiment, Storage (206) has large capacity to store the captured image for further data management.
In another embodiment, Local Server (101) is allocated the data as per the day to day captured images.
In another embodiment, Cloud Server (102) is to release the burden of local server, it is used to transfer the entire data of local server and after allocation of data it sends the signals/ notification to each and every student.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Fig. 1: Edge Assisted Vision Device (100)- This device deals with the detection of the multiple objects (students) and local server and cloud server are connected to the edge device to send the notification of attendance system.
Fig. 2: Working Diagram
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
Automated Attendance System using facial identification from Deep Learning Convolutional Neural Networks to develop the facing attendant system to be more effective and the mechanic of the system, which students can easily verify.
The system is capable of capturing multiple objects (students) with the help of edge devices in a single frame and identifying the individual based on the trained dataset. By using neural networks and pattern recognition attendance of multiple students can be marked automatically.
The overall architecture of the system comprises input in terms of images through camera, edge assisted vision device, Local Server 101 and Cloud Server 102. This system captures the images of a group of students and identifies their presence to log the attendance information on the cloud storage.
In this system, the basic process would involve detection of faces of the students present in the classroom automatically by the local server through a vision device already placed in the classroom. The detected faces are now sent to the cloud server for further processing. Whereas neural networks are used to uniquely identify the facial images. now response of successful attendance is sent to the students detected by the system
Convolution neural networks are known for their effectiveness in recognizing and classifying images. Object detections, face recognition etc., are some of the areas where CNNs have a wide scope and hence rightly used. A CNN takes in an input image, assigns weights to different objects and features in an image and thereby recognizes in this manner. CNN is a classification algorithm and the processing needed before execution is extremely low. CNN has the ability to learn features on its own after training the model for a while. The convolutional neural network identifies the various faces captured by the edge assisted vision device (GPS + Camera + RTC). Results are sent to the cloud server's database for sending responses and for future reference.
The Edge Assisted Vision Device 100 captures images of the students in the classroom. The control unit forwards this input to the AI for identification. Results of the AI are sent to the control unit for generating response. The information is then stored in the database for future utilization. Before discussion of utilization, Edge Assisted Vision Devices comprises the following components whereas control unit, GPS system 205, Camera 204, RTC 203, storage 206, neural network 202 and supply 201 etc.
In terms of utilization, this system is based on the identification of multiple objects or images then comes to control unit 200 so it makes the sequence of storage and neural network with the help of bidirectional method based on the supply 201 system. In parallel maximum data comes from the GPS 205 where it provides the location velocity and time synchronization. Camera 204 generate various frame of group patterns and RTC 203 gives the real time communication.
Best Method of working:
Edge Assisted Vision Device (100)- This device deals with the detection of the multiple objects (students) and local server and cloud server are connected to the edge device to send the notification of attendance system.
Control Unit(200)- Control unit makes a sequence of all modules related to edge assisted vision device.
Supply(201)-Supply system is the main energy source where it deals with the power generation and the current will be passed over the devices.
Neural Network(202)- Neural network capture the biological feature and convert into the data
RTC(203)- It provides the real time communication over the network
Camera(204)- It captures the multiple objects which is inbuilt in the edge device
GPS(205)- The manages the location of the objects
Storage(206)- It has large capacity to store the captured image for further data management
Local Server (101)- It will be allocated the data as per the day to day captured images
Cloud Server (102)- To release the burden of local server, it can be used to transfer the entire data of local server and after allocation of data it will send the signals/ notification to each and every student.
ADVANTAGES OF THE INVENTION:
This invention can mark the attendance of students in a group. The novelty is capturing the image of a group and segregating their presence in the form of attendance in one go.
The invention will help in marking the attendance in masses in real time entrance of the students.
It will reduce the consumed time period in taking attendance.
It will send the notifications of presence or absence of each student after every class.

We Claim:

1. An automated attendance system for multiple students using neural network comprises Edge Assisted Vision Device (100), Control Unit (200), Power Supply (201), Neural Network (202), RTC (203), Camera (204), GPS (205), Storage (206), Local Server (101); and Cloud Server (102) , wherein the system deals with the detection of the multiple objects (students) and local server and cloud server are connected to the edge device to send the notification of attendance system.
2. The system as claimed in claim 1, wherein said Control Unit makes a sequence of all modules related to edge assisted vision device.
3. The system as claimed in claim 1, wherein said Power Supply (201)- is the main energy source where it deals with the power generation and the current is passed over the system.
4. The system as claimed in claim 1, wherein said Neural Network (202) captures the biological feature and convert into the data; and said RTC (203) provides the real time communication over the network.
6. The system as claimed in claim 1, wherein said Camera (204) captures the multiple objects which is inbuilt in the edge device; and GPS (205) manages the location of the objects.
7. The system as claimed in claim 1, wherein said Storage (206) has large capacity to store the captured image for further data management.
8. The system as claimed in claim 1, wherein said Local Server (101) is allocated the data as per the day to day captured images.
9. The system as claimed in claim 1, wherein said Cloud Server (102) is to release the burden of local server, it is used to transfer the entire data of local server and after allocation of data it sends the signals/ notification to each and every student.