Smart Attendance Monitoring System system

Through this project, the primary aim is to create a smart system for contactless attendance monitoring at schools and offices. During the covid pandemic, the world tried to minimize physical contact as much as possible. Most of the offices around the world use fingerprint systems for checking in to the office, while educational institutes do manual roll calling.

The smart attendance monitor will identify faces and thus monitor the attendance. Thereby eliminating the need for manual attendance marking. For schools, the system can be further made to alert parents that their children had reached the classroom. The system will be very convenient as it could be wall mounted at the entrance of classrooms or office rooms. Students and staff can either come in front of the camera to detect their faces but this system can be also made to identify faces from a crowd. Thus could be even used at industries.

Aupera Face recognition app is used to create a face recognition model. A gallery made using images of students at college.

Setup and install the app

I followed the instructions of Get Started Documentation to download the app image and and flashed the image to a microSD card.

Before you can run the Face Recognition app, you need to create an access key, in the form of a cred.json file, from the Xilinx Appstore.

Starting the Face Recognition app

Next, copy the cred.json file to this directory: /D0/aup-pkgs/cred.json. From the KV260 command prompt, run the following commands to load the face recognition app:

sudo xmutil listapps
sudo xmutil unloadapp
sudo xmutil loadapp aupera-kv260-fr

Now you can start the app with the following command:

sudo som-start.sh

Create a gallery for Face Recognition

Before you can run the face recognition with the AI Client Software, you must create a gallery of faces.

With a face gallery, you can now run the face recognition to a incoming video stream. At the AI Client, select a video stream, select the gallery via the dropdown and then click the "Run Face Recognition" button. You then clieck the "View Live Result" button in order to view the similarity predictions. I wait for few minutes before the predictions appear.