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The global facial recognition market size was evaluated at $3.8 billion in 2020 and is expected to reach $8.5 billion in 2025, growing at a CAGR of 17.2%.
However, the costs of facial recognition software are challenging to estimate. There are hidden expenses that companies tend to overlook and, as a result, surpass the allocated budget.
Facial recognition systems are considered the most reliable among biometric identification forms, such as fingerprints and iris recognition.
But the technology has its challenges. The facial recognition process often occurs in an uncontrolled environment with variable lighting conditions and dynamic backgrounds. Other factors affecting recognition quality include facial expressions, a person’s age, and ethnicity.
After detecting a face, facial recognition software reads the facial geometry, which encompasses around 80 different elements. The key features include the distance between the eyes, eye socket depth, cheekbone shape, and jawline length.
When the analysis is complete, the tool will generate a facial signature as a mathematical formula and will compare it against other faces in the repository.
Facial recognition systems have many exciting applications in different industries.
Companies need to carefully consider their hardware choices to avoid getting their hopes high with a software solution that their devices can’t handle. But at the same time, firms shouldn’t overpay for computational resources that they will not use.
To have a working facial recognition system, you will need to procure cameras, switches, kilometres of cables, and servers for data storage and processing (unless you’re planning to use the cloud). All these utilities will add up to your facial recognition system’s price.
The camera type and its location depends on the desired coverage, image quality, and angle of view.
Examine the location where you want to install cameras. In the case of poor lighting conditions, opt for devices with built-in features that can compensate for the lack of light and still produce images that your facial recognition software can work with.
Also, some facial recognition algorithms require 3D cameras. You might consider purchasing cameras that come with pre-installed computer vision software installed and can accomplish tasks like pre-processing and face detection.
If the chosen format is not powerful enough, it will cause delays and quality degradation.
To identify a person posing in front of a camera, we need two neural network models – a face detector and a face recognizer. We can process around six frames per second. With these requirements, one could make do with a low-cost graphics processing unit (GPU), and even a central processing unit (CPU) might suffice. However, if we complicate this task by tracking people’s trajectories and actions or even increasing the number of cameras, we will have to procure a more powerful and expensive GPU.
Facial recognition solution vendors allow client companies to use their APIs, but you still need to integrate them into your system.
You can turn to custom software vendors to facilitate integration and build a client-side application, which allows you to reap the full benefits of the facial recognition software. For example, integrating a tool that only does face detection and identification will cost you at least $3,000, and this number will increase with the scope of the facial recognition solution.
If a company operates from a remote location where different communicating objects are positioned far apart, you will need to establish a reliable communication channel. Such high-quality cables can be even more expensive than servers and cameras and will account for a large portion of the total costs of a facial recognition system.
Some off-the-shelf solutions, such as Face++, set their prices based on the classifiers used, with more complex classifiers being more expensive.
Custom facial recognition solutions: The system may include classifiers, such as face detection, face verification, face grouping, similar face search, etc. The more models you accumulate, the more it will cost. But the number of classifiers is only one of several attributes impacting complexity.
Other parameters include the solution’s scalability, number of images being processed, security requirements, availability, and fault tolerance.
System complexity directly influences the costs of facial recognition software. You can deploy a tool with limited functionality for a few thousand dollars, while a highly complex and secure solution will cost you up to $1,000,000 and even more.
The location of operations also influences the total price. If you need to survey large warehouses with hundreds of cameras, hardware costs will form a large part of your expenditures. But even with complex solutions, you can plan ahead to ensure the allocated budget is wisely spent.
All about facial recognition tech.
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