facerecognitionattendancesystem-171229223331 (1).pptx
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This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. The methodology used was the waterfall model. The system architecture includes phases for training (registration), testing (attendance), and recognition. It was a learning experience that explored image processing and artificial intelligence.
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Face recognition attendance system
This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes.
facerecognitionattendancesystem-171229223331.pdf
This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes and strategies.
facerecognitionattendancesystem-171229223331.pptx
This document summarizes a face recognition attendance system project. The project uses face recognition technology to automate student attendance tracking. Key aspects of the project include defining face recognition, describing the project scope of providing an automated attendance system, reviewing the waterfall methodology and data flow diagrams, explaining the system architecture including the training, testing, and recognition phases, documenting the database design, and demonstrating the system through screenshots of the registration, image capture, recognition, and attendance generation processes. The summary concludes with reflections on the learning experience of carrying out a real-world software project from initial planning through working software.
face recognition attendance system-171229223331.pptx
This document describes a face recognition attendance system project. The system uses face recognition technology to identify students from captured images and mark their attendance automatically. Key aspects of the system include student registration, image acquisition, face detection, face recognition, a database to store student information and images, and an interface to view recognized faces and generate attendance reports. The project followed a waterfall development methodology and used various data flow diagrams and system architecture diagrams. Implementing this real-world face recognition application provided valuable learning experiences and insights into industrial software development processes and strategies.
Project_Presentation1.pptx
The document presents an automatic attendance system using real-time face recognition. It discusses biometrics and why face recognition was chosen. The system works by capturing student faces with a camera, encoding the images, and comparing the encodings to a database to identify the student and mark their attendance in an Excel file. Benefits include automation, cost-effectiveness, and increased security over traditional paper-based systems. Potential applications include classrooms, security access, ATMs, and healthcare. The conclusion discusses improvements over previous methods and potential future enhancements.
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This document describes a facial recognition-based attendance management system. It discusses how existing manual attendance systems can be replaced with a facial recognition system that uses computer vision, machine learning, and web development. The system allows students to register, have their images captured and a dataset created. It then uses HOG and SVM algorithms to detect and encode faces, and recognize students in real-time to mark their attendance with entrance and departure times. The system has modules for registration, managing attendance information and student records. It provides attendance reports and allows admins to add images and train the model for improved accuracy.
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This document presents a project on a face recognition system. It provides an abstract describing the use of biometric security systems like face detection and recognition to provide verification and identification capabilities. It then outlines the various sections that will be included in the report, such as introduction, methodology, tools/technologies, applications and future scope. The methodology section describes using an Agile development approach and details the requirements analysis, data modeling, and process modeling steps. Computer vision, image processing and machine learning tools and technologies are also listed.
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The document describes a proposed real-time image-based attendance system using facial recognition in Python. It involves four main steps: 1) capturing images using a webcam, 2) preprocessing the images by converting them to grayscale, 3) applying facial recognition algorithms like Haar Cascade and LBPH to detect and recognize faces, and 4) storing the attendance data in a database like CSV files. Previous related works that implemented similar systems using techniques like OpenCV, Viola-Jones, and deep learning algorithms are also discussed. The proposed system aims to provide an accurate, efficient and user-friendly alternative to traditional paper-based attendance methods.
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Face recognition attendance system
This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes.
facerecognitionattendancesystem-171229223331.pdf
This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes and strategies.
facerecognitionattendancesystem-171229223331.pptx
This document summarizes a face recognition attendance system project. The project uses face recognition technology to automate student attendance tracking. Key aspects of the project include defining face recognition, describing the project scope of providing an automated attendance system, reviewing the waterfall methodology and data flow diagrams, explaining the system architecture including the training, testing, and recognition phases, documenting the database design, and demonstrating the system through screenshots of the registration, image capture, recognition, and attendance generation processes. The summary concludes with reflections on the learning experience of carrying out a real-world software project from initial planning through working software.
face recognition attendance system-171229223331.pptx
This document describes a face recognition attendance system project. The system uses face recognition technology to identify students from captured images and mark their attendance automatically. Key aspects of the system include student registration, image acquisition, face detection, face recognition, a database to store student information and images, and an interface to view recognized faces and generate attendance reports. The project followed a waterfall development methodology and used various data flow diagrams and system architecture diagrams. Implementing this real-world face recognition application provided valuable learning experiences and insights into industrial software development processes and strategies.
Project_Presentation1.pptx
The document presents an automatic attendance system using real-time face recognition. It discusses biometrics and why face recognition was chosen. The system works by capturing student faces with a camera, encoding the images, and comparing the encodings to a database to identify the student and mark their attendance in an Excel file. Benefits include automation, cost-effectiveness, and increased security over traditional paper-based systems. Potential applications include classrooms, security access, ATMs, and healthcare. The conclusion discusses improvements over previous methods and potential future enhancements.
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The document summarizes a digital quiz system designed to minimize errors and save time in oral exams. The system consists of a judge and four participants. The participant who presses their button first in response to a question will be displayed and able to answer. If answered correctly, their score will increase on the display. Otherwise, their score remains unchanged. The circuit has two parts - one for participant selection and answering, and one for the judge to control scoring. The purpose is to efficiently evaluate participants while saving time and promoting accuracy and integrity.
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官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Software Engineering and Project Management - Introduction, Modeling Concepts...
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学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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7:个人信誉贷款加10分
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Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
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- Create S3 bucket.
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- Validate access.
- Exploiting IAM PassRole Misconfiguration
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- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
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- Create role with administrative privileges.
- Allow user to assume the role.
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Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Comparative analysis between traditional aquaponics and reconstructed aquapon...
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facerecognitionattendancesystem-171229223331 (1).pptx
- 1. Face Recognition Attendance System Jigar Patel (44) Amey Mohite (61) Naomi Kulkarni (65) Shivani Sharma (93) PROJECT: DUMMY Group No: 8
- 2. Agenda 2 • Definition • Scope • Project Status • Methodology • Data Flow Diagrams • System Architecture • Database Dictionary • Database Diagram • Demo • Designing of System (Snapshots) • Learning and experience
- 3. Definition Face Recognition is a biometric method of identifying an individual by comparing live capture or digital image data with the stored record for that person. Face Recognition Attendance System is marking of attendance based on this technology. 3
- 4. Scope 4 • Provides an automated attendance system that is practical, reliable and eliminate disturbance and time loss of traditional attendance systems. • Present a system that can accurately evaluate student’s performance depending on their recorded attendance rate.
- 6. Methodology • Waterfall Model is a sequential approach, where each fundamental activity of a process represented as a separate phase, arranged in linear order. • In the waterfall model, you must plan and schedule all of the activities before starting, working on them (plan-driven process). 6
- 9. DFD level 1.1 Student Registration 9
- 10. DFD level 1.2 Image Acquisition
- 11. DFD level 1.3 Face Detection 11
- 12. DFD level 1.4 Face Recognition 12
- 13. System Architecture Training Phase: Registration 13
- 14. System Architecture Testing Phase : Attendance 14
- 16. Database Dictionary 16 Field Name Data Type Length Constraint Description Roll_no Int 3 Primary key Student roll no Name Varchar 20 Not null Name of student Date Date 10 Not null Date of the attendance Time Time 10 Not null Time of the attendance Attendance Varchar 7 Present or Absent Attendance of a student Images .pgm 100 Size must be of 11KB Images of students
- 18. Log in The administrator needs to enter the username and password in order to start this system. 18
- 19. Home 19 Home page of the system which consists of Registration and Attendance button
- 20. Registration Form 20 On clicking the REGISTER button, registration interface is provided.
- 21. Registering Images On successful login, the interface to capture images appears. On clicking the CAMERA ON button, the dialog boxes appear to ask about the device type and the resolution type 21
- 22. Registration After the camera settings have been selected, the student’s image appears on the axes. 22
- 23. Registration 23 When the capture button is pressed the image has been captured and preview is shown into other axes highlighting the detected face.
- 24. Training set Multiple faces are captured while registration with different expression for more accuracy. 24
- 25. Attendance Time On clicking the GENERATE ATTENDANCE button, the recognition process starts and the detected faces are recognized. 25
- 28. Learning and Experience 28 From scratch to working software, carrying out real-world software projects in our academic studies helps us to understand what we have to face in industry. It was a wonderful experience working on Face Recognition Attendance System with enthusiastic and like-minded people wherein we explored a part of Artificial Intelligence, i.e. image processing, which relates to our system from capturing images, detecting faces, storing them in a database, extracting facial features, recognizing them and generating attendance through different algorithms, books, websites and with the guidance of our guide. We have learned most of the industrial strategies used for completion of project by keeping accounts of time, quality, and budget. This project was a door to a Stairs of Success towards the bright Software Engineering career.
- 29. 29