Automating the employees attendance system with Artificial Intelligence and Machine Language may solve several challenges. Read on to know more….
Recording the employees attendance with the accurate time is a major challenge for every organization. But with the smart application of Artificial Intelligence (AI) and Machine Language (ML), various challenges can be resolved.
There are several issues with the traditional employees attendance system. Some of the issues are employees may check in with the conventional attendance system promptly, but they reach their work desk after several minutes and start working with their job. In this case, the organization will be losing several man-hours per worker every day. Some of the other issue faced by several organization is that of ghost employees and proxy attendance.
Traditionally, the employees attendance system is implemented manually, a practice that often led to false records and friction between supervisors and employees. Identity cards and punch cards come with their own problems, and are often misplaced.
To address these issues, one can develop a facial recognition-based time and attendance system and a breath analyzer for catching anyone who turns up at work drunk.
Facial recognition-based Time and Attendance (T&A) system, roots out all these problems by capturing an employee’s identity at lightning speed and instantly recording his or her attendance. Once an employee’s face is registered into the T&A system, the cameras in the system recognise his or her face when the person appears, instantly marking attendance and granting access. This one-time registration unlocks access at multiple locations in large offices, saving time and solving the problems associated with tailgating. For this process, one can use the Microsoft’s facial recognition technology, Face API. This cloud-based technology not only delivers face-detection algorithms, but is also highly precise in matching multiple attributes of the captured image against the database of images at the backend.
The face recognition technology can also be enabled on supervisors’ mobile phones, tablets and even CCTV cameras to mark employees’ attendance in places where the system has not been installed. Artificial Intelligence and Machine Learning also play a major role in many of these detection and identification technologies. Using it in its webcams, the organization can develop it to capture facial features from CCTVs and apply the technology to mark attendance. While the system can be built as a cloud model, to address the challenge of the time taken to process the images, one should also have to install an on-premise facial recognition system.
Back at the organization where man hours are being wasted, one can use the CCTV cameras and employees can be asked to look at these when they walk past them before starting work. The facial recognition technology embedded in the system tracks the workers’ movement across the plant and hence is able to determine the cause for any delay in employees reaching their work areas.
A crucial feature of this technology is that it makes data available in real time. This is a huge advantage over other time marking systems such as biometric or ID cards, which synchronise the data with a time-lag. This real-time monitoring helps optimise workers based on real-time needs. For instance, a retail shop can look at its footfall and reduce or increase its customer-service staff appropriately. Moreover, unlike other attendance-marking models, this cannot be scammed easily and can be integrated with systems like IoT sensors and health monitoring systems. The system can work even when it is offline, which is useful in remote locations without internet access.
To safeguard employees’ privacy, organizations should make sure that its software saves only binary digits and not images. The software should only compare the binary digits with the saved, encrypted data at the backend to confirm the identity of the employee.
This could also help organizations to tackle the problem of ghost employees and of workers attending multiple shifts.