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Integrating Face Recognition for Attendance Tracking in Macadamia Plantations
Introduction
A leading macadamia plantation operator faced difficulties in accurately tracking employee work hours across multiple remote farms. With no internet access and a transient workforce, the company needed an efficient, offline attendance system that minimized disruptions and ensured smooth daily operations.
Challenges
- No Internet Access: The system had to function entirely on local devices, storing all data until an internet connection became available for synchronization.
- Efficient Workforce Management: Temporary and seasonal workers required a fast, frictionless check-in system. Even brief delays due to false rejections in Face Recognition or Liveness Detection could lead to productivity losses, as daylight working hours were limited.
- Minimal Spoofing Risk: Given the remote setting, sophisticated spoofing attempts (e.g., silicone masks or high-resolution screen replays) were unlikely. Thus, liveness detection only needed to perform basic checks without adding unnecessary complexity.
- Windows-Based Integration: The solution had to be deployed on Windows-based tablets already in use by the company, requiring integration with a .NET application.
Solution
The partner integrated 3DiVi Face SDK into .NET-based time-tracking application, offering:
- Employee Registration & Template Management: Secure storage of biometric templates.
- Work Time Logging: Employees check in and out using face recognition, ensuring authenticity.
- Offline Functionality & Synchronization: Data is stored locally and automatically uploaded when an internet connection is available.
- Server-Side Integration: A backend system aggregates work hours for payroll processing.
Implementation
The global architecture consists of both a client application and a server-side component. The server stores data on all registered employees and logs all time-tracking events, which are later used to calculate payroll.
The server-side face recognition pipeline includes:
1️⃣ Video stream analysis – The system detects and tracks faces in real-time.
2️⃣ Active liveness check – A quick liveness detection is performed for the primary person in the frame.
3️⃣ Best-shot selection – If the check is successful, the best-quality facial image is selected.
4️⃣ Time-tracking event creation:
The server-side face recognition pipeline includes:
1️⃣ Video stream analysis – The system detects and tracks faces in real-time.
2️⃣ Active liveness check – A quick liveness detection is performed for the primary person in the frame.
3️⃣ Best-shot selection – If the check is successful, the best-quality facial image is selected.
4️⃣ Time-tracking event creation:
- If an employee is checking in, the system searches the database and logs the event using the tablet's system time.
- If a new person is being registered, their facial template is added to the database.
Results
Reduction in Disciplinary Issues
By accurately tracking employees and minimizing fraudulent attendance claims, the company saw a drastic reduction in disciplinary problems related to time logging. Instances of registering non-present individuals or falsifying attendance were virtually eliminated.
Increase in Labor Productivity
With the quick and efficient check-in system, employees were able to start their shifts promptly without unnecessary delays from failed or slow registration processes.
Improved Accuracy of Work Time Logging
The integration of facial recognition with active liveness checks ensured that only present employees could be logged and tracked. This significantly improved the accuracy of work time logs, reducing errors from manual records.
By accurately tracking employees and minimizing fraudulent attendance claims, the company saw a drastic reduction in disciplinary problems related to time logging. Instances of registering non-present individuals or falsifying attendance were virtually eliminated.
Increase in Labor Productivity
With the quick and efficient check-in system, employees were able to start their shifts promptly without unnecessary delays from failed or slow registration processes.
Improved Accuracy of Work Time Logging
The integration of facial recognition with active liveness checks ensured that only present employees could be logged and tracked. This significantly improved the accuracy of work time logs, reducing errors from manual records.
Conclusion
Integrate 3DiVi Face SDK into your time and attendance system to boost employee productivity and reduce fraudulent claims. Supporting C++, Java, Kotlin, Swift, and Flutter on both Android and iOS, the solution offers top-rated accuracy in NIST FRVT (99.73%) and high performance, providing the precision needed for flawless time-tracking.
Ready to get started? Book a free consultation with our experts to discuss how we can adapt 3DiVi Face SDK to your needs!
Ready to get started? Book a free consultation with our experts to discuss how we can adapt 3DiVi Face SDK to your needs!
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