Description:Field of the Invention
This invention relates to identity card identification with image processing.
Background of the Invention
Although technology has strengthened security, manually analyzing forged information when an individual enters and exits establishments remains a challenge. A Raspberry Pi-powered system can record an individual's information, including their ID card, in a database for future reference. The system will include a camera to capture student and individual activity, such as leaving and entering the premises with their ID card. This technique improves building safety by eliminating counterfeit ID and impersonation. It is faster, more precise, and stronger.
The Raspberry Pi-driven system offers a cost-effective and multipurpose solution to boosting security. With automated identification card recording and verification, this reduces the chances of human error as well as hastens the entering process. The camera integration permits real time surveillance and validation, ensuring authorized persons only access the premises. This is highly significant in educational institutions, corporate offices and other places where safety is important.
The system’s database becomes a useful asset for future reference or audits. In case of any security breach or investigation, it provides recorded data that can be quickly recovered and analyzed for essential insights. The system’s ability to adjust itself to different environments and needs has made it scalable enough to address diverse security concerns. Altogether, deploying this Raspberry Pi-powered system signifies an immense step forward in managing safety through merging technological advancements with effectiveness thereby creating a secure environment.
EP3608810A1 A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.
RESEARCH GAP: Speed: Real-time data processing and recording go on inside the system, so wait times are highly low.
CN107978044B The invention provides a high-precision identification system based on a face identification technology and an RFID technology, which comprises an ultrahigh frequency RFID subsystem, a face identification subsystem and a management server; the management server comprises a processing unit and a storage unit, wherein a site member information database is stored in the storage unit; the ultrahigh frequency RFID subsystem is used for reading the serial number information of the RFID card in a specified place in real time; the processing unit sends the face information of the registered user corresponding to the RFID card number information to a field personnel information database; the face recognition subsystem is used for acquiring a face image of a user at an access channel in real time; the processing unit compares the received face image of the user with face information of the registered user in the field personnel information database to judge whether the user is the registered user. The invention can generate the face information database of the small sample by utilizing the RFID technology, is used for face recognition comparison of field personnel, and improves the face recognition comparison efficiency and accuracy of the field personnel.
RESEARCH GAP: Accuracy: Utilizes the most advanced artificial neural network models that accurately detect and recognize ID cards.
CN109446875B The invention discloses an intelligent passenger security check system, which comprises a face recognition subsystem, an information subsystem, a security check subsystem and a self-service verification subsystem; the self-service verification subsystem is used for acquiring personal information of passengers; the face recognition subsystem is used for extracting the face features of passengers; the information subsystem is used for carrying out verification according to the face characteristics and the personal information. The system can be adopted to easily store the personal information of the passenger and the security check information in a correlated way; the corresponding personal information and the security check information are effectively called, and the response time of calling the information is greatly shortened. The security inspection of personnel and luggage is realized, and the manpower and time for security inspection are saved.
Research Gap: Enhanced Security: Automatic and correct recording of entrance and exit reduces the risk of unauthorized entrance or exit.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to identity card identification with image processing.
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.
Present invention discloses a system of Identity card identification with image processing comprises a plurality of QR Code (1.1, 1.2, 1.N), Device (Authenticating), Wifi Module (3), Cloud Server (4), Raspberry Pi 3V+ (5), Neural Stick (6), Camera (1280 x 720) (7), Keyboard (8), Mouse (9), LCD Screen (JHD 204A) (10) and 12v 3amp Lithium Polymer (Battery) (11) wherein the camera is appropriately positioned to capture crisp ID card images, which simplifies the procedure.
In another embodiment, the microprocessor is flexible and adaptable to different contexts, marks an important leap forward in safety management by combining technical developments with efficacy.
In another embodiment, the neural computing stick speeds inference, allowing for rapid text and image recognition and robust device speed up the neural network inference.
In another embodiment, the deep learning models carry out relevant recognition tasks with high levels of precision.
In another embodiment, the Wi-Fi module is embedded into the microprocessor to enable communication with the cloud server, where all the data is securely stored.
In another embodiment, the LCD is integrated with the microprocessor and provides visual instructions and feedback during ID card scanning; It is easy to use, displaying instructions, system status, and confirmation notifications in addition permitting students and staff to leave.
In another embodiment, the use of Li-Po battery is selected because of their high energy density with regards to mass and volume, and long life results in providing dependable power to the system components, which keeps the system in operation at all times in case of cut-offs and maintenance of security and data recordings without interruption.
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.
Regarding technological development, the need for advanced security measures becomes imperative, especially in environments such as learning institutions, corporate offices, and other secure premises. Current manual methods of entering personal details on the visitors by the security personnel are time-consuming and expose data to pilferage or tampering. So, through this study, we propose a system flawless in its operation because it uses the powerful technologies of Tensorflow integrated with YOLOv5 for text, image, and card detection and is running on the Raspberry Pi platform.
The developed system can automate, record, and verify entry and exit by the usage of their ID cards. Great emphasis will be laid on creating a system whereby high precision, fast working, and better security are ensured by reducing risks linked to the manual maintenance of records. A Raspberry Pi is placed as a central processing unit at the system's core. A Raspberry Pi can host wide-ranging applications with its tiny size. It is used to act as the system's brain, coordinating activities for all attached peripherals and running the image and text recognition algorithms.
The Raspberry Pi is interfaced with a high-resolution camera to capture the image of the ID cards presented by people.
The camera is placed correctly for the images of ID cards to be clear and without impediments. Thus, simplifying the process of the picture subject. Then, the images are analyzed by a neural compute stick, a robust device that speeds up neural network inference, imputed on the Raspberry Pi. The stick dramatically boosts the ability of the system to process. In a short instance and adequately, it can detect and recognize text and images on the ID cards. TensorFlow and YOLOv5 are utilized to implement and deploy deep learning models that carry out relevant recognition tasks with high levels of precision.
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:
Figure 1: General Architecture of the system
Figure 2: Detailed description of the project
Figure 3: Algorithmic Functionality of the system
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.
Regarding technological development, the need for advanced security measures becomes imperative, especially in environments such as learning institutions, corporate offices, and other secure premises. Current manual methods of entering personal details on the visitors by the security personnel are time-consuming and expose data to pilferage or tampering. So, through this study, we propose a system flawless in its operation because it uses the powerful technologies of Tensorflow integrated with YOLOv5 for text, image, and card detection and is running on the Raspberry Pi platform.
The developed system can automate, record, and verify entry and exit by the usage of their ID cards. Great emphasis will be laid on creating a system whereby high precision, fast working, and better security are ensured by reducing risks linked to the manual maintenance of records. A Raspberry Pi is placed as a central processing unit at the system's core. A Raspberry Pi can host wide-ranging applications with its tiny size. It is used to act as the system's brain, coordinating activities for all attached peripherals and running the image and text recognition algorithms.
The Raspberry Pi is interfaced with a high-resolution camera to capture the image of the ID cards presented by people.
The camera is placed correctly for the images of ID cards to be clear and without impediments. Thus, simplifying the process of the picture subject. Then, the images are analyzed by a neural compute stick, a robust device that speeds up neural network inference, imputed on the Raspberry Pi. The stick dramatically boosts the ability of the system to process. In a short instance and adequately, it can detect and recognize text and images on the ID cards. TensorFlow and YOLOv5 are utilized to implement and deploy deep learning models that carry out relevant recognition tasks with high levels of precision.
The system has an LCD screen for visual prompts and feedback to the user on how to scan the ID card. The LCD is integrated with the Raspberry Pi, and the interface will be done with the help of the LCD screen. It shows the instructions, the status of the system, and the confirmation messages, which, in general makes the system user-friendly. When the student or employee wants to leave, they just show their ID card to the camera. The application captures the image using TensorFlow and YOLOv5, extracts the details required, and, on a cloud-based database, makes an entry of the exit time.
On re-entry, an ID card is presented with a recording of the time of re-entry to have a complete and accurate log of movement by a person. The Wi-Fi module is embedded into the Raspberry Pi to enable communication with the cloud server, where all the data is securely stored. It provides an easy and quick way to transfer data between the Raspberry Pi and the cloud server.
The cloud server holds all record data and could be obtained at any time for future reference and analysis. This cloud-based approach ensures data security: information is stored off-site and can be protected from local hardware failures. The design incorporates a Li-Po (Lithium Polymer) battery as a power source, enabling it to function even when power goes off. The use of Li-Po batteries was selected because of their high energy density with regards to mass and volume, and long-life results in providing dependable power to the system components, which keeps the system in operation at all times in case of cut-offs and maintenance of security and data recordings without interruption. In this regard, other peripherals added to the Raspberry Pi, such as the keyboard and mouse, provide for human access to configure and execute system control. These peripherals are the bridges that enable the system administrator to interact with the job of setting up, maintaining, and troubleshooting the system. They conveniently aid in manipulating and configuring the system to effect the specific requirements.
The process begins when a person arrives at the system for ID verification. The system uses a high-resolution camera interfaced with the Raspberry Pi to capture the photo of the shown ID card. This image is then fed for processing, the process taking place previously by using TensorFlow and YOLOv5 models, which had been pre-trained on the data students or employees of the organization. Text and images on the ID card can be detected and recognized with very high accuracy by these models.
After the image is taken, the ID number, name, and photo are extracted from the ID card. From the extracted details, checking is now done on the records being loaded on the cloud-based database. Upon matching the details, the system will authenticate and record the entry and exit time of an individual, otherwise showing an error message and completing the process.
On successful authentication, the system logs the entry or exit time. It sends the information to the cloud server for storage and future reference, ensuring a complete and precise record of all movements. The neural compute stick connected to the Raspberry Pi makes a fantastic world of difference about speeding up the processing of neural network inferences for quick and smooth recognition tasks.
In terms of architecture, all components are interconnected to form a cohesive system. A camera is attached to the Raspberry Pi, which is connected to the attached LCD, neural compute stick, Wi-Fi module, and Li-Po battery. The due rights and proper interfaces used with the cables establish sound, reliable, and intact communication networking services among the components. The Wi-Fi module grants an entrance to touch the horizons of the cloud server to provide optimum and smooth data transfer and storage services. The keyboard and mouse afford an interface for the administrator to manage the system in such a way that it will work efficiently and effectively. This system has proven to be a great advancement in ensuring that the safety and protection of premises are carried out properly while also providing a reliable and efficient method for keeping track of the movement of individuals within a certain range.
Present invention comprises a plurality of QR Code (1.1, 1.2, 1.N), Device (Authenticating), Wifi Module (3), Cloud Server (4), Raspberry Pi 3V+ (5), Neural Stick (6), Camera (1280 x 720) (7), Keyboard (8), Mouse (9), LCD Screen (JHD 204A) (10) and 12v 3amp Lithium Polymer (Battery) (11) wherein the camera is appropriately positioned to capture crisp ID card images, which simplifies the procedure.
In another embodiment the microprocessor is flexible and adaptable to different contexts, marks an important leap forward in safety management by combining technical developments with efficacy.
In another embodiment the neural computing stick speeds inference, allowing for rapid text and image recognition and robust device speed up the neural network inference.
In another embodiment deep learning models carry out relevant recognition tasks with high levels of precision.
In another embodiment the Wi-Fi module is embedded into the microprocessor to enable communication with the cloud server, where all the data is securely stored.
In another embodiment the LCD is integrated with the microprocessor and provides visual instructions and feedback during ID card scanning; It is easy to use, displaying instructions, system status, and confirmation notifications in addition permitting students and staff to leave.
In another embodiment the use of Li-Po battery is selected because of their high energy density with regards to mass and volume, and long-life results in providing dependable power to the system components, which keeps the system in operation at all times in case of cut-offs and maintenance of security and data recordings without interruption.
In another embodiment the system is configured to verify the authenticity of identification cards against a stored database and the system is configured to provide real-time feedback to users through the LCD screen.
In another embodiment the system is configured to securely store visitor data on a cloud server; and the system is powered by a rechargeable Li-Po battery.
In another embodiment the system is configured to be controlled and managed via a keyboard and mouse.
ADVANTAGES OF THE INVENTION
Scalability: Can be deployed anywhere with minor changes.
Always-On Operation: Works without glitch even when the power is out, for it runs on a Li-Po battery.
Ease of Use: User-friendly interface with visual prompts and feedback.
Data Security: Information is stored in a database hosted on the cloud securely.
Cheap: Uses cheap and readily available components.
Flexibility: Easily interfaced with current security systems.
Future-proof: New models and technologies can be easily upgraded.
, Claims:1. A system of Identity card identification with image processing comprises a plurality of QR Code (1.1, 1.2, 1.N), Device (Authenticating), Wifi Module (3), Cloud Server (4), Raspberry Pi 3V+ (5), Neural Stick (6), Camera (1280 x 720) (7), Keyboard (8), Mouse (9), LCD Screen (JHD 204A) (10) and 12v 3amp Lithium Polymer (Battery) (11) wherein the camera is appropriately positioned to capture crisp ID card images, which simplifies the procedure.
2. The system as claimed in claim 1, wherein the microprocessor is flexible and adaptable to different contexts, marks an important leap forward in safety management by combining technical developments with efficacy.
3. The system as claimed in claim 1, wherein the neural computing stick speeds inference, allowing for rapid text and image recognition and robust device speed up the neural network inference.
4. The system as claimed in claim 1, wherein the deep learning models carry out relevant recognition tasks with high levels of precision.
5. The system as claimed in claim 1, wherein the Wi-Fi module is embedded into the microprocessor to enable communication with the cloud server, where all the data is securely stored.
6. The system as claimed in claim 1, wherein the LCD is integrated with the microprocessor and provides visual instructions and feedback during ID card scanning; It is easy to use, displaying instructions, system status, and confirmation notifications in addition permitting students and staff to leave.
7. The system as claimed in claim 1, wherein the use of Li-Po battery is selected because of their high energy density with regards to mass and volume, and long-life results in providing dependable power to the system components, which keeps the system in operation at all times in case of cut-offs and maintenance of security and data recordings without interruption.
8. The system as claimed in claim 1, wherein the system is configured to verify the authenticity of identification cards against a stored database and the system is configured to provide real-time feedback to users through the LCD screen.
9. The system as claimed in claim 1, wherein the system is configured to securely store visitor data on a cloud server; and the system is powered by a rechargeable Li-Po battery.
10. The system as claimed in claim 1, wherein the system is configured to be controlled and managed via a keyboard and mouse.