Choose the Right Face Recognition Solution for Your Organization

What is Face Recognition?

Facial recognition is a type of biometric technology that enables operators to rapidly identify people that appear in a video. With facial recognition software, operators can extract facial features from faces in live or recorded video and compare them against a database or watchlist of digital images (or extracted facial features) of persons of interest. If a face is matched to someone on the watchlist, a real-time alert can be triggered automatically to notify security staff, who then must assess the match for accuracy and determine how to respond. In addition, some face matching solutions operate using a “whitelist” of authorized persons. If a non-authorized is detected, an alert will trigger.

Beyond detecting potential threats or validating authorized individuals, face matching technology can be used for locating missing persons; identifying and factoring out employees when people counting in a retail scenario; and accelerating post-event investigations. While these solutions are traditionally, operated by law enforcement or physical security professionals, as the technological landscape has evolved with the adoption of ultra-high-definition cameras and the growing sophistication of artificial intelligence applications, facial recognition technology has become more available, robust, and accurate – and thus more widely adopted.

Bottom line: face recognition is an important technology – with varying types and applications – that allows persons of interest to be proactively monitored or quickly engaged depending on the situation.

Ready to dive into Demystifying AI-Driven Face Recognition? Watch our on-demand webinar and get empowered to navigate this complex landscape.

2 Types of Face Recognition

The marketplace for facial recognition is evolving and booming worldwide, and consumers have their choice of technology to implement. Of course, not all facial recognition technologies are created equal, nor are they all applicable for every situation. For this reason, it is important to understand that face recognition can be divided into two solution types: those for cooperative access control scenarios and those for non-cooperative, or “in the wild” surveillance scenarios.

Access control refers to implementations that manage entry of certain people to certain areas. For example, to protect the assets of a bank vault, visitors may be required to pass through a facial recognition terminal to verify their identity. “In the wild” face recognition encompasses face matching using CCTV video cameras intended for monitoring an area. This type of face recognition is more challenging because subjects are not necessarily looking directly at a camera, cameras may not be optimally positioned, or provide high enough resolution to ensure a high level of accuracy.

3 Types of Matching

In addition to these two scenarios of facial acquisition, there are 3 ways of matching the acquired face with reference images. In controlled settings, a person usually supplies the system with an identifying object (such as an ID Card) which tells the system against which face reference the person must be matched. This type of recognition is called 1-to-1 matching or “verification,” because the acquired face is being matched to a single predetermined reference image.

If the system has no prior identifying data about the person to be recognized, the system must try to match the face against an entire watchlist or a large subset of it. This is called 1-to-Many. 1-to-Many can be used in what the industry is calling “frictionless access control”, where a person on an approved access list can pass through a specific point without having to stop to be scanned as in traditional access control. 1-to Many matching is also used for “In the Wild” matching, commonly used when finding a missing person.

The final category of face recognition matching is Many-to-Many. Many-to-Many is the most anonymized form of face matching because the system is simply analyzing one anonymous image to and reidentifying it, a critical function for certain business intelligence applications such as understanding the number of unique visitors at a retail location.

Overcoming Face Recognition Limitations

Face recognition is extremely valuable for identifying persons of interest in video; however, it is not always possible to use facial recognition technology. First and foremost, some municipalities and countries do not permit the use of face recognition technology due to privacy regulations. Yet, even where face recognition can be used, video surveillance cameras often capture faces “in the wild,” in less-than-ideal conditions: In some cases, a camera installed isn’t optimally placed or doesn’t record high enough resolution video to enable face recognition. In other cases, subjects may be facing away from a camera, have partially obscured faces, or they may be walking quickly in low-light conditions, all of which compromise the ability to provide accurate face recognition. Without optimal conditions the technology cannot always deliver the desired results.

Factors to Consider When Investing in Face Recognition

When choosing a facial recognition solution, organizations should consider how the technology will be used, to ensure that it will meet their unique needs while also supporting responsible use in their area of jurisdiction. Additionally, buyers should ask themselves whether their current infrastructure is suitable to enable face recognition, and whether additional or replacement hardware is needed to support the basic image quality requirements. They should also consider whether existing integrations will need to be updated or reconfigured to support the new face recognition capabilities.

Advantages of Comprehensive Video Content Analytics

In cases where facial recognition technology cannot be operated effectively or responsibly – organization’s efforts to locate or identify persons will be stymied, and investigations will be stalled. In these jurisdictions, organizations would benefit from a comprehensive video analytics, which offers other video search and alerting capabilities to deliver results with speed and accuracy. Based on Deep Learning techniques, advanced video analysis software can detect, identify, extract and catalog objects in video footage according to classes and attributes such as gender, appearance similarity, color, size, and direction of movement. This functionality enables operators to search for objects or people using filters such as “men with black hair and a red short-sleeved shirt walking South”.

Comprehensive video investigation software also enables users to configure real-time alerts based on class, attribute, or facial combinations, so that security or law enforcement staff can be notified when someone in a video camera feed matches a description. Another benefit is that, over time, aggregated video metadata can also be visualized by a video content analytics system – presenting long-term data within dashboards, heatmaps, and reports to empower managers to make better data-driven decisions for long-term planning.

By integrating a comprehensive and extensible video content analysis solution that includes face recognition, organizations can enjoy the benefits of both solutions, applying the technology as permitted and useful to transform video into data that is searchable, actionable and quantifiable.

Watch our on-demand webinar, Demystifying AI-Driven Face Recognition, and join us as we debunk common face and biometric recognition technology myths and empower you to navigate this complex landscape.

Editor’s note: This post was originally published in December 2019, and has been refreshed and updated for accuracy.