Epigen Powers Facial Recognition in the Cloud with SingleStore – Case Study
Epigen Technology depends heavily on SingleStore as part of its core toolkit. “Without SingleStore, I can’t do what I do,” says Terry Rice, founder of Epigen.
Visit SingleStore at AWS re:Invent booth #1905 in Las Vegas, November 27-30.
Epigen Technology uses software to innovate in many fast-changing areas of technology, including cybersecurity, analytics, machine learning, artificial intelligence (AI), and the emerging area of cognitive computing. Now, Epigen has developed a framework for facial recognition in the cloud that combines AWS S3, SingleStore, machine learning, AI, and visualization.
Terry Rice, founder of Epigen, is a noted IT consultant and is trusted by senior figures in business and government to deliver solutions to challenging problems, including national security threats. He’s also a fifth-generation Marine. Terry graduated early from the University of Oklahoma with dual degrees in Information Systems and Statistics. After finishing college, he enlisted as a Marine, following a family tradition going back more than a century. He served for six years.
Terry then moved into roles as an IT consultant and software architect for companies, such as Capgemini, Lockheed Martin, Network Solutions, and Verizon. He’s also worked for government, serving as a solutions architect for the Department of Homeland Security. In his current role, Terry is helping to lead the development of innovative systems in several areas. He speaks at conferences on blockchain, big data, machine learning, and AI. And he uses SingleStore as a core part of Epigen’s toolkit.
Recognition Faces Challenges
Facial recognition is one of the most challenging topics in AI. Facial recognition for isolated static images is improving – as experienced by anyone who uses Facebook, which can often suggest names for most of the faces in a photograph of family or friends.
The Facebook example shows some of the characteristics that make facial recognition easier:
A well-lit, high-resolution, static image.A small list of candidate names for each face (the user’s Facebook friends).Subjects not trying to disguise themselves.Low consequences for failure – in the Facebook case, a non-identification or misidentification is an annoyance, or even humorous, rather than a serious problem.
However, Epigen’s clients in areas such as the military, law enforcement, and air transportation need facial recognition that works under increasingly challenging circumstances, including working at scale and using video frames, not just static images.
Currently, facial recognition is being used at some airports to verify the identity of passengers on a per-flight basis. The comparison is a four-step process:
For a specific flight, passport images for all ticket holders are vectorized and stored.People who appear at the gate to board a flight are photographed live.Live passenger photos are compared to the stored passport photos.If a live passenger photo doesn’t match any of the stored passport photos, the passenger is asked to verify their identity as a match against their passport.
After years of testing, this system is now in use at more than a dozen airports and at land ports of entry to the United States. In one recent case, it prevented a passenger who was using a passport that was valid, but not their own, from boarding a flight at Dulles Airport in Washington DC.
The current approach requires that each live photo be compared to as many as nearly a thousand passport photos (given that an Airbus A380, the largest passenger airplane, can carry more than 800 passengers). This is only the beginning. Current, and potential future, users of facial recognition systems need more capability.
Epigen is working on a series of improvements:
Higher accuracy – for the current, one in a thousand use case. The system used to check passengers against the passport photo database is only about 85 percent accurate. There is a short-term goal to improve accuracy to 97 percent, then further improvement will be required from there.Compare against other databases – for use cases of one to thousands and one to millions. Today, each live photo is only compared to the passport photos of one specific flight’s passengers. In the future, the system should also compare live photos to databases from Immigration and Customs Enforcement, the FBI, other law enforcement agencies, motor vehicle departments, and others.Use full-motion video – lower-resolution, moving images. Video cameras are everywhere, even more so in some countries than others. Automatically spotting wanted people on the move is an important goal for law enforcement. One high-priority use case, hard to deter by other means, is recognizing missing and kidnapped children. AI-based recognition from video could augment existing systems, such as the AMBER Alert system used in the US.
One target application for video is to shorten lines at airports. If people can be identified and matched against passport photos as they move through the terminal, they can be, in essence, pre-approved. The small number of people who raise concern can be contacted by officials before reaching a gate. This pre-checking can avoid the need for people to queue at entry points, potentially eliminating the long lines at Customs, for instance, when entering a country.
Throwing Resources – and SingleStore – at the Problem
How can facial recognition be made to work better, and to scale to handle more comparisons, at higher speeds, and to handle video?
Epigen is developing a scalable architecture for facial recognition. Requirements are strict. For instance, “We need less than 10ms response time, round trip,” according to Rice.
SingleStore is a great fit for this kind of challenge. The Pipelines feature combines rapid data ingest and data processing. “I like SingleStore because it’s lightweight and runs on bare metal,” Terry says. “I can make the technology disappear.”
