Introduction
Developments in biometrics are swift, bringing the promise of increased security to a wide range of applications. Traditional security measures like personal identity cards, passwords/PINs, and keys are readily lost or stolen, while biometric technology uses an individual’s unique bodily traits, making it much more secure. Learn AI free course to know more and apply in your project for artificial intelligence. Although biometric identification has been available for a long time (through fingerprints, for example), other biological data and iris scanning merit closer examination.
In recent years, online face recognition has become increasingly common. Mobile phones are a typical example of this phenomenon. Numerous people all across the world use this technology regularly to lock and unlock their mobile phones.
The potential benefits, however, go well beyond individual mobile devices and might have a huge impact on safety, security, and efficiency in many other sectors. Online face recognition is a captivating, all-encompassing, and gratifying technology. Therefore, it’s essential to know how it operates, how it may be deployed and maximized, what technical considerations and requirements are necessary, and what kinds of uses it can be put to.
What is Edge Face Recognition?
Edge face recognition is, in a nutshell, how the world recognizes an individual. It uses both Edge computing and Edge artificial intelligence.
In contrast to sending data to cloud data centres, which might be located hundreds of miles away, Edge computing enables processing to take place nearer to the data’s original location. Without an active data connection, this processing might happen locally on a device.
Meanwhile, AI provides the ability to swiftly and accurately make judgments based on otherwise complicated facts. Simply described, Edge AI is a form of AI that makes use of Edge computing by processing data locally.
To use Edge face recognition, picture processing and recognition are handled locally, on the devices themselves. Since these devices aren’t affected by the same network issues that plague cloud computing, they can function swiftly and precisely.
Edge-based face recognition is the ideal solution for most end users because of its cheap cost, flexibility, and scalability.
The Benefits of Edge Facial Recognition
With the help of Edge computing and Edge AI, technologies like facial recognition will make our lives easier, smarter, and safer. Lower operational costs, higher efficiency and usability, and better privacy and security are just some of the ways in which users of Edge face recognition systems may benefit from the usage of AI and Edge computing.
Biometric AI systems, like online face verification, require substantial computer capacity for cost-effective artificial intelligence. Edge devices have a lower price tag than cloud computing.
- Live face detection
Edge online face recognition may also identify and activate features that would otherwise be missed. Traditional face recognition rapidly returns time-consuming client requests for face detection. Moving photos and video around continually requires a lot of bandwidth, therefore this approach isn’t optimal.
- Prevents fraud in biometric systems
Edge AI can tell the difference between a genuine person and a computer-generated one. To trick a Face Recognition system, attackers employ both 2D and 3D (still images and videos).
Presentation Attack Detection (PAD) is a security mechanism implemented to prevent adversaries from evading detection by facial recognition systems. Incorporating more advanced multi-sensor technology with PAD at the Edge makes this potent security solution both easier to install and more responsive.
- Innovations in industrial design
Artificial intelligence at the Edge may function despite severe power limits, which can allow for a smaller, lighter design. By doing so, product designers are free to come up with novel configurations for face recognition more suited to the conditions of unique use cases.
- High-resolution photos
The accuracy of online face recognition systems relies significantly on the picture quality of the supplied faces. Edge AI enables instantaneous analysis of an image’s quality. Only high-resolution photos of people’s faces will be accepted into an Edge face recognition system.
Methods of Integration on Edge Devices
The most important choice when developing a face recognition edge device is selecting the appropriate chipset according to the intended application. While a high-end NVIDIA GPU chip, for example, may cost more upfront, it will be able to process hundreds of video channels simultaneously, cutting down on the need for expensive workstations to oversee a sizable building. On the opposite end of the performance spectrum, a low-cost SoC like MediaTek or Broadcom will only be able to do frontal face recognition at speeds of roughly five frames per second. However, for less demanding applications like door entry, it should be more than enough and cheaper.
GPU
Independent Graphics Processing Unit (GPU) chips are high-performance computers tailored specifically for online face recognition. For complicated, compute-intensive AI algorithms like face recognition, GPUs are the ideal solution because of their large memory, high memory bandwidth, and huge quantity of floating-point calculation capacity. Face detection in surveillance systems, which needs applying biometric authentication across many video channels concurrently, is another area where GPUs shine.
Operation Systems
Chipsets are often developed for use with certain software (OS). A high-quality online face recognition engine, ideally, would work with a wide variety of chipsets and operating systems. FaceMe supports more than 10 operating systems and one of the most extensive lists of chipsets available on the market.
- Windows
- Android
- Linux
Optimization of System Architecture
Facial recognition system design for high-performance workstations or PCs with GPU (or VPU) is notoriously challenging due to the huge volume of video data that must be transferred between the CPU, GPU, and RAM in real-time. The bad system architecture will make even the best facial recognition algorithm unusable or painfully sluggish. Minimizing the amount of data transferred between the central processing unit, graphics processing unit, and memory is an important design goal for any computer system.
Conclusion
We can all look forward to a better society thanks to advances in facial recognition technology. However, this cannot happen until people everywhere have had a greater opportunity to learn about the ethical implementation and develop a tolerance for companies that have publicly accepted AI as a fresh, safe norm.
The benefits of online face recognition go well beyond the hype around its potential dangers. By using automated, secure access control systems, organizations can ensure the safety of their personnel. What we’re seeing is shops improving their in-store experiences for customers. Manufacturers are streamlining access to their various secure locations to make them more user-friendly. Stronger authentication and state-of-the-art security procedures are being introduced by banks and fintech firms. This is only the beginning.
