Skip to content

Latest commit

 

History

History
113 lines (88 loc) · 5.35 KB

File metadata and controls

113 lines (88 loc) · 5.35 KB

Smart-Attendance-System-Using-Face-Recognition

In any organization, whether it be educational, industrial or corporate, attendance is an important aspect to evaluate the overall productivity of that organization. A proper attendance record will help to easily acquire any data related to particular individual or member and to maintain the services record. Attendance evaluation allows a greater degree of control over the individuals productivity and maintaining a proper validation of students/employees. The aim of Smart Attendance system is to design an efficient way to record and maintain student attendance data that will overcome the drawbacks of the traditional or manual attendance system. Attendance Management System is software developed for daily student attendance in schools, colleges and institutes. It facilitates to access the attendance information of a particular student in a particular class. A smart attendance system enables setting up the attendance workflows and maintaining a proper validation of students.

The applications of the project are as follows :

1. Face-Recognition:
a. Face recognition method is utilized to detect the presence of students in classroom.
b. The fetching and processing of images is done using raspberry pi and camera modules.

2. Student Access:
a. Provides features to students such as analysing their attendance and performance.
b. Students can get alerts via website.
c. Students can pass queries to respected faculties.

3. Faculty Access :
a. Faculties will have a transparent data of all students.
b. Faculties can pass on alerts/announcements.
c. Student’s queries can be handled and answered.

Problem Statement

There are several problems with traditional manual attendance systems that smart attendance systems can overcome. These problems include:

  1. Time-consuming
  2. Inaccurate
  3. Limited accessibility
  4. Lack of security
  5. Limited analytics

Distinct Features

  1. High Accuracy
  2. Contactless
  3. Real-time Tracking
  4. Easy to Use
  5. Scalability
  6. Integration with Other Systems

Utilities

  1. Hardware oriented
  2. Face Recognition & Associated libraries
  3. Associated Website
  4. Peripherals

Working

The System consist of Hardware component and Software component.

Hardware Components consists of:

  1. Camera Module
  2. RFID
  3. Raspberry Pi
  4. LED display.

Software Components consists of:

  1. Web Application
  2. Database System

System Design

Hardware Components and Results

UI Design