There is an internet story of a software engineer who, while visiting Disneyland, went on a ride and was then offered – by a theme park employee – a photo of himself and his girlfriend to buy – with his credit card information already linked to it. The engineer emphatically stated that he had not entered any of his personal or credit card information on any of the theme park’s registers. So, he determined, based on his professional experience, the system had to be using facial recognition technology to access and activate his personal facial and credit card information. He had never signed an agreement allowing the Mouse & Co. to do so, and believed that this use was illegal. He also stated that he believed Disney was sharing information related to facial recognition technology with the military.
As it turn out, he may not be wrong or very far off from the truth.
To understand how his claim of passive facial recognition might work let’s first define facial recognition technology: Facial recognition software (FRS) can pick someone’s face out of a crowd, extract the face from the rest of the scene and compare it to a database of stored images. In order for this software to work, it has to know how to differentiate between a basic face and the rest of the background. Facial recognition software is based on the ability to recognize a face and then measure the various features of the face.
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up facial features. FRS defines these landmarks as nodal points. Each human face has approximately 80 nodal points. Some of these measured by the software are:
•Distance between the eyes
•Width of the nose
•Depth of the eye sockets
•The shape of the cheekbones
•The length of the jaw line
These nodal points are measured creating a numerical code, called a faceprint, representing the face in the database.
Next, let’s review how facial recognition occurs. (In the past, FRS use was limited to 2D facial images, and subject to many environmental factors, such as lighting or blurring, that restricted its use to primary law enforcement agencies for comparative analysis v. existing pictures of the subjects of interest. We are now well past that stage and into comparing live 3D images to networked databases worldwide.)
3D Facial Recognition Facial recognition software uses a 3D model, which provides more accuracy than its 2D predecessor. Capturing a real-time 3D image of a person’s facial surface, 3D facial recognition uses distinctive features of the face — as outlined above — to identify the subject. These areas are all unique and don’t change over time.
Using depth and an axis of measurement that is not affected by lighting, 3D facial recognition can even be used in darkness and has the ability to recognize a subject at different view angles with the potential to recognize up to 90 degrees (a face in profile).
Using the 3D software, the system goes through a series of steps to verify the identity of an individual.
Acquiring an image can be accomplished by digitally scanning an existing photograph (2D) or by using a video image to acquire a live picture of a subject (3D).
Once it detects a face, the system determines the head’s position, size and pose. As stated earlier, the subject has the potential to be recognized up to 90 degrees.
The system then measures the curves of the face on a sub-millimeter (or microwave) scale and creates a template.
The system translates the template into a unique code. This coding gives each template a set of numbers to represent the features on a subject’s face.
If the image is 3D and the database contains 3D images, then matching will take place without any changes being made to the image. However, there is a challenge currently facing databases that are still in 2D images. 3D provides a live, moving variable subject being compared to a flat, stable image. New technology is addressing this challenge. When a 3D image is taken, different points (usually three) are identified. For example, the outside of the eye, the inside of the eye and the tip of the nose will be pulled out and measured. Once those measurements are in place, an algorithm (a step-by-step procedure) will be applied to the image to convert it to a 2D image. After conversion, the software will then compare the image with the 2D images in the database to find a potential match.
Verification or Identification
In verification, an image is matched to only one image in the database (1:1). For example, an image taken of a subject may be matched to an image in the Department of Motor Vehicles database to verify the subject is who he says he is. If identification is the goal, then the image is compared to all images in the database resulting in a score for each potential match (1:N). In this instance, you may take an image and compare it to a database of mug shots to identify who the subject is.
Facial Recognition Systems Uses
Law enforcement: Aside from the obvious background identification and history of arrested suspects, l.e. uses the system to capture random faces in crowds to match to their terrorist databases. .
Government agencies: Some government agencies have also been using the systems for
– monitor voter fraud
– eliminate “buddy punching” (The practice of a coworker signing for a friend or displaying that friend’s id for UPC processing. )
– tracking foreign visitors and frequent flyers (The Department of Homeland Security has implemented a program called US-VISIT, United States Visitor and Immigrant Status Indicator Technology, aimed at foreign travelers gaining entry to the United States. When a foreign traveler receives his visa, he will submit fingerprints and have his photograph taken. The fingerprints and photograph are checked against a database of known criminals and suspected terrorists. Likewise, the TSA is runs its Registered Traveler program through FRS.
Other potential applications currently in use include ATM and check-cashing security and access to your own lap/desk top via the monitor’s FR program.
To get back to our irate software engineer, he is correct in identifying Disneyland’s use of facial recognition software and sharing it with the United States Department of Defense. This collusion is referred to as Operation Mickey Mouse (not joking) and has been in effect for decades. Who would suspect the family friendly theme park of being a de facto arm of the government?
Now the vast majority of us will never really notice how much facial recognition has creeped into our lives — but if there is a foul-up, you can expect it to be a big deal. ALL government FR dbases will have to be updated if a modification (e.g., surgically enhanced faces) occurs.
Our operatives: Situationally aware.
As always, stay safe.
- FBI sharing facial recognition software with police departments across America (EndtheLie.com)
- FBI Sharing Facial Recognition Software With Police Departments Across America (theintelhub.com)
- How To Beat Facial Recognition Technology (gizmodo.com.au)
Filed under: facial recognition | Tagged: biometric, Department of Motor Vehicles, Disneyland, Facial recognition system, Registered Traveler, security, United States, United States Department of Defense |