Attendance System by Face Recognition

introduction:

In the context of technology, we live in a great scientific time that requires us to use technological tools and means from a scientific point of view to achieve the greatest possible benefit. The feature of facial recognition is considered one of the modern sciences that we will benefit from mainly in our modest project.

Project Idea:

Using a facial recognition camera, powered by Raspberry Pi4, and linking it to a database to prove attendance and absence using Python programming.

Attendance system:

Attendance system based on the algorithms of the front face by analyzing the saved and captured images by the camera and comparing them and showing the result and saving it in a database to prove attendance and departure

Raspberry Pi 4

Two HD Microsoft cameras

Touch screen display for Raspberry 7inch

Two speakers

Two Push buttons

One Speakers power supply(1A)

One Raspberry power supply (3A)

Computer mouse

One Box

Two Camera installation base

1-Raspberry Pi 4

https://www.amazon.com/dp/B07TC2BK1X/ref=twister_B...

2-Two HD Microsoft cameras

https://www.amazon.com/Microsoft-T3H-00013-LifeCam...

3-Touch screen display for Raspberry 7inch

https://ar.banggood.com/Raspberry-Pi-4B-LCD-Capaci...

4-Two speakers

we bought it from local store

5-Two Push buttons

we bought it from local store

6-One Speakers power supply(1A)

we bought it from local store

7-One Raspberry power supply (3A)

we bought it from local store

8-Computer mouse

we bought it from local store

9-Box

we went to a local store to make it

10-Two Camera installation

we went to a local store to make it

first:

we upgrade the system to python 3.7

second:

We used the opencv2 library in programming and the code was downloaded successfully without any problem

The process of downloading OpenCV2 took about two days. At the beginning, we downloaded the library then we update it . it took us from the morning to the night to get updated

After that, we downloaded the second command, then we added the download command from the source, in the seventh command it took time until the next day we found it had been downloaded and then we added eight steps that took about an hour and the download finished.

http://mitchtech.net/raspberry-pi-opencv

Dataset: this code when we run it , we need to enter user id number after entering the id number it will start taking pictures and save it in the file named dataset , you can chose the number of pictures taken and the frame size

the number of pictures taken in the code is 30

the fame size is 1.3

https://iotdesignpro.com/projects/face-recognition...

this code transfer the pictures that has been saved in the dataset file to algorithms so that the system can identifies the data.

https://iotdesignpro.com/projects/face-recognition...

We added the facial recognition code. It is a code that recognizes the front face by comparing the images saved in the data set file and the currently captured images, and the process is done through the image conversion code into the algorithm's measurements and showing the saved name in case of finding a match. if it didn't find a match it will say unknown.

Note:

In the code we can change the id number to the real names of the person but the name need to be connected to the id number in the dataset

example:

in the code of the dataset we will enter user_1 but in the face recognition code we delete id_1 and wrote Mohammad to show it on the frame .

extra note:

We can adjust the sensitivity of the camera and how many time checking.

https://iotdesignpro.com/projects/face-recognition...

here we add two libraries and they are (pyttsx3&espeak).We add text to speech to say the names.

we had some problems in the library of speck but the project supervisor helped us with it

in this step we add the codes of the second camera and the text to speech and pushbuttons

we add CSV library for the database and its run with the LibreOffice

and this is the final project video

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