Class Pass

Inspiration

Classpass was inspired by the desire to modernize and streamline the school lunch attendance system, where the traditional manual scanning of student IDs can often be slow and prone to errors. We wanted to replace this with a faster, more secure, and touchless solution using cutting-edge technology. By combining a Raspberry Pi with an Arduino Uno R3, we could build a device that integrates hardware and software seamlessly, leveraging facial recognition to automate attendance and eliminate the need for a paid worker to verify IDs.

What it does

Classpass automates attendance in the school lunch system by using a Raspberry Pi, running facial recognition software, to quickly and accurately identify students based on their facial features. We utilize the camera module connected to the Raspberry Pi to capture the student’s face and the Arduino Uno R3 to control user inputs. The LCD display shows the student's status, and the 3D-printed case provides a portable, durable housing for the device. This system ensures a faster, more efficient, and hands-free process for both students and staff.

How we built it

We built Classpass by integrating a Raspberry Pi with the OpenCV library to implement facial recognition. The Pi processes images from the camera module and sends the data to the Arduino Uno R3, which manages the hardware interface and controls the LCD display. A 3D-printed case houses all components, ensuring the device is portable and protects sensitive electronics. The Arduino handles I/O operations, while the Raspberry Pi processes the facial recognition algorithm and manages communication with the school’s attendance system for accurate tracking.

Challenges we ran into

A key challenge was calibrating the facial recognition software to work in varying lighting conditions, requiring adjustments to the OpenCV parameters to ensure accuracy in different environments. Additionally, ensuring smooth communication between the Arduino and Raspberry Pi was complex, as the devices had to work together to handle both the facial recognition and the user interface via the LCD. The integration of the Raspberry Pi and Arduino with the various sensors and displays required careful debugging to ensure reliability and responsiveness.

Accomplishments that we're proud of

We’re proud of the device's ability to perform real-time facial recognition, providing quick and accurate attendance marking. Using the Arduino Uno R3 and the Raspberry Pi, we achieved seamless integration between hardware and software. The system is completely portable, housed within a custom 3D-printed case, allowing us to bring the solution directly to the lunchroom or other areas within the school with ease. We were able to develop a solution that combines both the Raspberry Pi’s processing power and the Arduino’s control features.

What we learned

Through this project, we gained a deeper understanding of how to integrate various hardware components, like the Raspberry Pi, Arduino Uno R3, and LCD displays, into a cohesive system. We also learned about the complexities of facial recognition, including how to fine-tune OpenCV’s algorithms to handle a range of environmental factors like lighting and angles. The hands-on process taught us a great deal about system design, troubleshooting, and how to combine computational tasks and hardware in a functional prototype.

What's next for Class Pass

Moving forward, we plan to refine the facial recognition algorithm on the Raspberry Pi, ensuring better accuracy under various lighting conditions and optimizing the speed of recognition. We also aim to improve the Arduino-Pi communication to make it more robust, possibly adding more features such as automatic syncing with the school’s lunch payment system. Future versions of Classpass could include further hardware integrations, such as RFID for additional security, and we'd like to test the device in real-world school settings to gather feedback and refine its functionality.