The first factor that you must take into account before using a system for facial recognition for attendance is the number of facial points that are being used for recognizing a face. When the technology was invented it either relied on around 90 facial points or looked at the distance between the eyes to recognize a face. At that time people used to judge how good a system is by looking at the number of points that were being used for such work. These thought processes are redundant right now. The advanced systems these days use deep neural networks which can function the way that the human visual cortex does.
The ML framework being used by the system
Every face recognition time attendance system needs a proper ML (machine language) framework for it to work properly. An ML framework determines the extent to which such a system is optimized for high performance. It also determines the way these neural networks have been deployed. It enables these systems to interface easily with other technology. It also makes the software capable of using all the hardware acceleration that they can get. This includes the likes of heavy parallelization and GPUs (graphics process units).
The camera being used for unregulated facial recognition
Your face recognition biometric machine need not have a sophisticated camera, which is rather costly as well. This is applicable only for algorithms that have become outdated. These days you can use cheap IP (internet protocol) cameras and get the job done as well. If your facial recognition system has an algorithm that utilizes neural networks, it would not need an expensive and high-end camera. These are robust systems and can adjust with the facial expression and mood of your workers as well. They can also work just fine in noise and lightning.
Does the system have an on-premise facility?
You need to know and understand the fact that the face recognition attendance system, which you are using, may not be able to function from a remote location. The best example of such a setting would be an airport terminal. Over there you would need an on-premise facility. Your vendor may tell you that its system works on a technology that helps the system adapt to the environment in which it is working. It could be working based on machine learning. However, do you know how the software has been built? Therefore, you need to be clear at your end.
Has the software been tested?
You need to make sure that the software that you are using for facial recognition for attendance has been tested properly. This is also referred to as Proof of Software. This determines if the software, which you have bought, has been built for actual applications or not. Thus, this is an important test of the quality of the software that your vendor is trying to sell to you. You should know that if the product is an efficient one it would not have to be tested for months just so that it can get Proof of Software.
When it comes to buying a face recognition time attendance system this is an important criterion for sure. Since you are investing money in the software you have to be as careful as you possibly can be. Research is extremely important in such a situation. This will help you find out if the cost being asked for the product is at all justified or not. You can be sure that if a system is good enough it would indeed cost you some money. This means that such software would have the likes of ML algorithms and deep neural networks.