Presently, facial recognition systems are gaining a lot of traction. They are getting deployed in several public places for safety reasons, private properties to access control systems, and now also as an application on mobile phones for various purposes.
Facial recognition technology has widespread usage. The system has become so inevitable that no other technology can replace it in the future. As a result, developing such systems is tricky and needs careful planning for each case individually. As an entrepreneur, if you need such an application for your business, you must enlist the costs and expenses that are incurred to develop such a system along with the probable pitfalls.
The blog shall help you to know about each stage of your cost plans for developing facial recognition apps. Also, we shall discuss several problems that you will need to solve while in the process. It is not only about developing apps, but it is also about the hardware architecture that will allow the app to work efficiently.
You need to address the below matters before you head on to develop a facial recognition system.
- Hardware Configuration
- Camera Choices and Their Locations
- Edge Device Usage
- Personification and Possible Bias in the Pretrained Model
- Software Scalability
- Image Retrieval Speed
Before beginning with the facial recognition app development project, the tech solution provider must consider hardware configuration, cameras, and edge devices, etc, and such important aspects.
At the time of building a facial recognition security system, you must know that the optimal hardware configuration differs from task to task. The suitable hardware should be chosen with great care. When the chosen hardware lacks enough power, it leads to highly undesirable delays in processing in some quality solutions. For other tasks that don’t require a lot of computational resources, the choice of powerful hardware could cause a too expensive solution, which could be no longer in demand because of its price.
Camera Choices and Their Location
When you put enough care and become cautious to complete a specific task, you must always check with the type of cameras to be deployed. The configuration choices vastly depend on quality image requirement, angle of view, and coverage area. Besides, when choosing video quality and several cameras, the network bandwidth must be taken into account. This has commonly become one of the crucial bottlenecks in building high-quality facial recognition systems.
Edge Devices Usage
Most of the facial recognition system is dependent on heavy, deep neural networks. Such networks most often need GPUs for evaluation with appropriate speed. The modern facial recognition systems emphasized that companies have machines with CPU and GPU. To ensure the work completion, the servers would require extra support in time and demand some location. Jetson devices solutions are used to reduce the support for end customers. Human support also gets minimized.
A lot of nuances must be considered while setting up optimal hardware for the system. It is also hard to choose it without experience. Hence, what we believe is that the experienced specialists create a final architecture solution for a specific facial recognition system. They determine the optimal configuration for every particular case.
Here we shall discuss major software issues coming around the face recognition technology industries.
Robust facial recognition application is created with the deeper learning-based methods. These deep learning models are notoriously known as very data-hungry. When a company lacks its data the very first step is to use open-source datasets. The dataset licensees are worth reading before using the data to train a face recognition model.
Personification and Possible Bias in the Pretrained Model
You must consider the demographic profile of the system users. Most of the open-sourced data sets comprise white adults, the final reports are quite biased and may not work properly on other races. To understand in a better way, you must collect additional private datasets consisting of similar demographic profiles to the one you expect in real life. The model bias can be reduced by adopting fine tuning on the collected data.
Companies deploy facial recognition systems to expect them to become scalable and expand enough to a wider audience. The software hence needs to possess the high flexibility architecture of a system to be very thoughtfully devised. Overall, the software part can be built in a way to get quick updates to a larger number of users with inexpensive costs. Based on our practical experiences, we found that we need a well-designed system architecture from scratch to extend the software that is not designed to become scalable.
Image Retrieval Speed
Several face recognition systems face serious bottlenecks when it performs searches in the database that consists of millions of data samples. In this scenario, the company ensures that the algorithm used to search through a database is much more efficient and scalable.
How Much Does it Cost?
The cost of developing a robust and scalable face recognition application complexity depends on the project requirements. Besides, many variables have the total price of the bespoke face recognition application.
To provide the best solutions to clients and to meet the challenges, we take up such projects through the below stages.
- Business objectives analysis
- Data analysis and identification of the scope of work
- Definition of high-level architecture and appropriate tools and technologies selection
- Risks identification
- Building proof of concept
- Definition of the expected results as well as costs and resources estimation.
The investigation stage permits idea validation at an early stage, risk minimization of a project failure, and substantial savings of money. The Cost of the Investigation stage is generally in the range of USD 10000 – 30000 based on the project size.
Both software and hardware challenges must be noted before going ahead with design plans of a facial recognition application system. Have an experienced team that can help you with the uneasy work. It helps a lot in the long run. Good and robust facial recognition applications are developed with great time, effort, and solid technical skills with great expertise. This ensures that the project is very scalable, reasonable, and reliable to take it forward for further processes.