Unraveling the Versatile Deployment Architectures of Face Recognition Technologies

Face recognition technology is revolutionizing various industries with its diverse range of applications. From bolstering security and enhancing user authentication to delivering personalized customer experiences, the adaptability of face recognition is truly remarkable. This article explores the key factors of the deployment architecture for the successful implementation of a face recognition system. 

Every face recognition system is composed of five key components, each serving a distinct purpose: 

1. Facial Image Capture: This component involves capturing facial images, which can be done through cameras, uploaded photos, and more. 

2. Face Detection and Feature Extraction: This process extracts facial features and plays a crucial role in accurate recognition. 

3. Gallery of Faces: This is the database containing pre-registered faces for identification. 

 4. Identification or Verification System: The core component that matches a probe face against the gallery to determine identity. 

5. Reporting System: How and where the results are presented to the user. 

The architecture of a face recognition system is influenced by several factors, including: 

• Verification vs. Identification: Is the user trying to verify the subject's identity or identify the subject? You can read more about the differences between the two in my previous article here. 

• Cooperative vs. Non-cooperative Subjects: The behavior of subjects and their willingness to cooperate will dictate the system requirements. There are distinct challenges faced when working with non-cooperative subjects. You can read more about this in my previous article here. 

• User Interaction: Who is the user? Is the subject using the system themselves (self-service) or is another person using the system on the subject or is it happening automatically? 

• Real-time vs. Batch Processing: Is the response needed immediately or batch processing for later reporting is acceptable? 

• User Notification: In real-time scenarios, does the user need to see the result, or does a notification need to be provided with the subject's details?  - A notification to an authorized person is relevant in case of identifying consented customers or blacklisted subjects. - Immediate feedback to the subject is relevant in cases where the subject is marking their attendance (self-service).   - Results need to be shown immediately in the case of a police officer (the user) trying to check if the subject is a history-sheeter. 

• Online vs Offline: Does the system have to support offline processing?  - In many instances, the recognition needs to happen offline in remote locations with no internet. For this, the face dataset is distributed and is in sync with a centralized server. This is common in many attendance capture applications.  - In some instances, such as the history sheeters, the face dataset cannot be distributed for security reasons and the search has to be performed on the server.  - In another instance, such as the army on the border using it to identify known terrorists, the entire system is totally offline and does not come online at all. 

• On-boarding Process: How are the subjects enrolled in the system? Even though only one photo of the subject is needed at #FaceTagr (explained here), there are different methods to get them enrolled. 

• Additional Features & Integration: Does the solution require more capabilities such as geo-fencing, anti-spoofing, access control, etc.? 

Architecting the Solution: 

Based on the above requirements, various components of the face recognition solution are designed, including: 

• Choice of Camera: Depending on the use case, different types of cameras are considered, from webcams to CCTV and mobile device cameras. 

• Edge Computing: Selecting the right devices for processing the video feeds from the cameras such as a GPU based edge processor or an Android mobile device. 

• Reporting Modalities: Determining how results are communicated to users, whether through online portals, mobile notifications, email alerts, or on-screen displays. 

• Server Infrastructure: Based on what processes are run on the edge vs central location, the server infrastructure is designed. 

The Role of the Core Face Recognition Engine: 

As is evident, there exists a multitude of permutations for the various components, and these permutations are highly dependent on specific use cases. While it may be considered more of an engineering challenge than a face recognition technology issue, it's crucial to recognize that the effectiveness of any solution is profoundly impacted by the underlying core face recognition system. For example, the deployment architecture is constrained by several key factors such as: 

• Hardware Requirements: Can the system run on various form factors without compromising on achieving the desired speed and accuracy?

• Camera Compatibility: Can the system process feeds from a variety of camera types? 

• Software Platform Compatibility: Is the engine compatible with multiple software platforms?

• Offline Capability: Can the function offline without being connected to the internet or the server? 

A flexible core engine paves the way for diverse deployment architectures. At #FaceTagr, our face recognition engine is both camera and device-agnostic, enabling its use across a wide spectrum of applications, from mobile phones of supervisors on construction sites to GPU cloud servers processing multiple CCTV camera feeds.

Face recognition technology's adaptability is one of its greatest strengths, allowing it to serve various use cases effectively. By understanding the key components and considering crucial factors, solutions can be architected that cater to an organization's specific needs, ensuring the successful deployment of face recognition technologies in diverse scenarios.