As we continue this series on physical and behavioral biometric technologies, Ravi Das addresses the topic of facial recognition technology and its many applications. This technology often makes headlines for its controversial nature. But, as the author explains here, facial recognition has numerous practical applications for businesses, airports, border control, and public safety.
Given the covert nature and easy deployment of facial recognition into non-biometric systems and technologies, it is no wonder that it has a wide range of market applications.
Probably the biggest application for facial recognition technology has been for the e-Passport, and to a certain degree, the National ID Card system, for those nations that have adopted it. For example, the International Civil Aviation Organization (ICAO) has made facial recognition templates the de facto standard for machine readable travel documents. Another popular application for facial recognition is for border protection and control. It is widely used in European countries for this purpose.
Also, along with iris recognition, facial recognition is being used quite heavily at major international airports, primarily for covert surveillance purposes by scanning for individuals on terrorist watch lists.
Despite the general public’s objections, facial recognition can be used very covertly at venues where large crowds gather, such as sporting events and concerts. Casinos often use facial recognition systems to identify and verify welcome guests and to spot “unwelcome” guests.
Facial recognition systems can also be used in conjunction with Closed Circuit Television (CCTV) cameras, and strategically placed in large metropolitan areas. The city of London is a perfect example of this, with a facial recognition/ CCTV camera system deployed at almost every street corner. Thanks to this vast security network, the London police were able to quickly apprehend and bring to justice the terrorist suspects involved in train bombings.
Facial recognition is also heavily used in conducting real time market research studies. For instance, it can be used to gauge a potential customer’s reaction to certain advertising stimuli by merely recording that individual’s facial movements.
Facial recognition technology is sometimes used in both physical access entry and time-and-attendance scenarios, but not nearly to the degree that fingerprint recognition is used.
The following case study is a concrete example of how facial recognition technology has been used.
Case Study #1: “Razko Security Adds Face Recognition Technology to Video Surveillance Systems”
Older generation video technology includes tools such as Multiplex/Time Lapse cameras, Digital Video Recorders (DVRs), as well as Network Video Recorders (NVRs). While these traditional technologies have proved their worth over time, the expense of maintaining them has proliferated for businesses of all sizes.
One strong, inherent disadvantage of this traditional security technology is that it is primarily a sophisticated archiving mechanism, meaning potential criminals and suspects can only be seen after the footage is recorded. Thus, they cannot be identified in real time and apprehended immediately. In other words, the potential criminal or suspect could very well leave the crime scene far behind and elude law enforcement officials long after the video footage has been carefully examined by security personnel.
Also, depending upon the size of the business, it is a huge expense to maintain a staff of security guards to identify the literally thousands of individuals that enter and exit a facility on a daily basis. But, with the addition of facial recognition technology to current Closed Circuit Television (CCTV) technology, the identification of potential suspects and criminals can occur in real time, and thus, apprehension can occur very quickly on-scene, often seconds after the crime has occurred.
Although it sounds complex, the idea behind all of this is quite simple. A facial recognition database of all potential criminals and suspects is created and implemented into the CCTV technology. As a particular individual walks past the CCTV cameras, his facial images are instantaneously compared to the database, and a potential positive match can be made.
Through the facial recognition technology developed by Cognitec, an alarm is sounded in real time, once a positive match is made between the facial recognition databases and the CCTV camera footage. This is perfectly illustrated by Razko Security. Its president, Ted Eliraz, was approached by Mike Kavanaugh, a Canadian Tire business owner, based out of Oakville, Ontario.
Their business requirements were as follows:
- To have multiple CCTV cameras with facial recognition technology implemented at various places of businesses.
- To have a central facial recognition database for ease of administration.
- The most important requirement: A reasonable cost.
With the facial recognition technology provided by Cognitec, the streaming video is displayed in real time, and when a positive match is made, the face of the potential criminal or suspect is displayed, along with any relevant law enforcement details and information about that individual.
There were some major hurdles to overcome when installing this biometrics-based system, in particular:
- Determining the exact locations of the CCTV/facial recognition cameras at each place of business.
- The effect of various lighting conditions from the outside environment.
After conducting a thorough systems analysis and design, the cameras were placed at the main entrance, the exteriors, and the glass doors at the various Canadian Tire locations. Canadian Tire ultimately had this technology deployed at additional locations throughout Canada, and the results proved to be very successful.
For instance, within the first six months of operation, four suspects were apprehended, and with further implementation of this technology at other Canadian Tire dealerships, the facial recognition databases of potential suspects and criminals has grown, resulting in a much higher apprehension and capture rate.
Up Next: The next article in this series will be a deeper dive into the technology behind facial recognition.
Ravi Das is a Cybersecurity Consultant and Business Development Specialist. He also does Cybersecurity Consulting through his private practice, RaviDas Tech, Inc. He also possesses the Certified in Cybersecurity (CC) cert from the ISC2.
Visit his website at mltechnologies.io
