QUESTIONS YOU SHOULD ASK BEFORE CHOOSING A FACIAL RECOGNITION SYSTEM

1. QUESTIONS YOU SHOULD ASK BEFORE CHOOSING A FACIAL RECOGNITION SYSTEM Driven by advanced neural networks and Artificial Intelligence, the Biometrics Industry has grown by leaps and bounds. What has grown further is its reach. It has gone from a luxury only affordable by the defence ministries of different countries to each smart phone that is made today. Facial recognition has gained the most traction over the last few years, as fingerprint sensors have already been sufficiently optimised. These systems have features which are offered at varying levels. Some simply match photos on a 2-D plane, while others factor in the depth of the face as well. There is a plethora of options to choose a facial recognition system from, and there are some key aspects to keep in mind when buying one for yourself. Here are a few things you should keep in mind: 1.The number of points on a face used for recognition In its initial stages, this technology relied on the number of facial points or the distance from one eye to the other to help in distinguishing a face. More the number of points used, the more superior the system. But they have since been outdated. Modern facial recognition systems have no reliance on such facial points or eye distance. They now come equipped with deep advanced neural networks which are capable of mimicking the human visual cortex’s complex capabilities of pattern recognition. They are also more robust to the user’s mood, lighting, and glasses. 2.The ML Framework used in the system Before going on to ask this question to the provider, you should know the different aspects that affect the functionality of a good ML Framework. Here is a concise list: 1.Determining the level of optimization of the network for performance 2.The process of the deployment of the neural networks 3.Ease of interfacing with a stack of another software 4.Usage of every available hardware acceleration including heavy parallelization and GPUs In most cases, this question finds its solution in an open source framework because developing an individual customized framework is a huge ask. Moreover, in the absence of expert developers, a huge number of computations can slow the system down. 3.The camera preference for face recognition which isn’t regulated Expensive and sophisticated cameras are required only for obsolete algorithms. You need not spend thousands of dollars for a camera when a cheaper IP camera worth a few hundred bucks can do the job just as well. The algorithms which require neural networks to run have no requirement for hi-tech cameras. Their robustness is good enough to detect the mood and expression of an individual, the lighting and any

2. background noise that may creep into the system, irrespective of the quality of the camera. 4.The test status of the software The PoC (Proof of Concept) is a big factor that helps in determining whether the software you have purchased can operate on applications in real life or not. It is important for you to test the software parallelly if possible. An efficient software won’t require months of testing as you may think. It delivers the result in a few hours.