New: Introducing SIA’s Core Principles for Facial Recognition Technology Use
SIA believes facial recognition must be used only for purposes that are lawful, ethical and nondiscriminatory. Explore our new principles for development and deployment of facial recognition tech across safety and security applications.View the Principles
Advances in computing power, combined with rapid improvements in the quality of photo and video technology developed by the security industry over the last 15 years, have allowed facial recognition technology to develop and mature. Use of facial recognition technology has benefited Americans in countless but underpublicized ways – helping law enforcement find missing children, fight human trafficking, find dangerous criminals and bring sexual predators to justice. At the same time, the technology allows individuals to quickly and conveniently prove their identity to enter a venue, board a plane, perform online transactions and seamlessly access personalized experiences.
Here are several examples of positive use cases of facial recognition technology and “success stories” highlighting how the technology can help protect against violence, theft or other harm.
Facial recognition technology can be used in emergencies where a suspected terrorist attack is imminent or underway, by dramatically reducing the timed needed by first responders and investigators to generate leads and identify suspects. This can improve response times, or ultimately help prevent or mitigate harm from such attacks – saving lives in the process.
Catching a Suspected New York City Subway Terrorist
New York City Police Department (NYPD) detectives used facial recognition technology to identify a man who sparked terror by leaving a pair of rice cookers in the Fulton Street subway station. Within minutes, detectives pulled still images of the suspect from security footage and used facial recognition software to compare them to mug shots in the NYPD’s arrest database. The system returned several hundred potential matches, and after multiple stages of human review, it took NYPD only one hour to identify the suspect.
The head of the NYPD facial recognition unit, Sgt. Edwin Coello, was quoted saying, “Five years ago you probably have endless detectives looking through videos and images of arrested individuals based on descriptions…It could take several hours or several days. This is the most important type of case that we’d see out there: a possible terrorist attack in NYC.”
Fighting Human Trafficking
Since 2015, the nonprofit group Thorn has provided a tool called Spotlight, which uses facial recognition among other technologies to help investigators find underage sex trafficking victims in online ads. Spotlight has reportedly been used in 40,000 cases in North America, helping rescue 15,000 children and identify 17,000 traffickers.
Saving a Sex Trafficking Victim
In 2019, a California law enforcement officer saw a social media post about a missing child from the National Center for Missing and Exploited Children. The officer used Spotlight to return a list of online sex ads featuring the girl. According to a story in WIRED, the girl had been “sold for weeks,” and the officer’s actions initiated a process that “recovered and removed from the girl from trauma.”
Facial recognition technology is deployed in dozens of airports across the United States and continues to grow. U.S. Customs and Border Protection (CBP), along with airport officials, matches passport photos to a database to verify the identity of thousands of travelers entering and leaving the U.S. each week. The technology is proving to be an important tool for border security.
As of June 2020, nearly 300 individuals have been intercepted attempting to enter the U.S. under a fraudulent identity. Beyond violating laws against illegal entry, passport fraud fuels many other types of crime, including human trafficking and drug smuggling.
A 26-year-old man on a flight from Brazil entered Washington Dulles International Airport and presented agents with a French passport. Agents used facial recognition to compare his passport photo to a database of known images with identities, which alerted CBP that the man was carrying a fraudulent passport. The agents discovered the man’s real identification card in his shoe, and it was revealed he hailed from the Republic of Congo, not France.
A Cameroonian woman posed as an American when trying to enter the United States with a fraudulent passport. The woman presented the CBP officer with U.S. passport in the name of a U.S. citizen, but when the officer ran the passport through the facial recognition technology, it was discovered that their identities did not match. After an additional verification, her true identity was exposed. Similarly, a week prior a woman was arrested after arriving on a flight from Ghana, for posing as a U.S. citizen with a fraudulent passport.
Facial recognition technology can be used in health care facilities in a variety of ways that protect the health and safety for patients and staff, including securing valuable lab equipment and providing touchless access so that only trained and authorized personnel can enter sensitive areas of a facility like “clean rooms” in order to eliminate risk of contamination.
During and post-COVID-19, there is considerable opportunity to use facial matching technology for access control to help ensure security and safety. Many hospitals have already implemented facial recognition to eliminate the need for front-line health care workers to swipe badges or type in codes to verify their credentials. Reducing contact with potentially contaminated surfaces is key when staff must be on high alert around the spread of germs and disease. Such technologies also can perform dual security and hygienic functions in more common settings as well, providing touchless access to virtually any workspace and giving an added layer of hygienic security, as opposed to handing over a pass card or touching a fingerprint scanner.
Law Enforcement Investigations
For well over a decade, federal, state and local law enforcement have successfully used facial recognition technology as an effective tool in thousands of investigations. Many public safety officials feel that this technology is becoming a game-changer for keeping our communities safe, pointing to instances where crimes would have never been solved or prevented without it.
Facial recognition helps investigators narrow searches for suspects more quickly, find missing children, rescue human trafficking victims, exonerate the innocent, achieve justice for victims, identify the deceased and benefit our communities in many other ways. In U.S. law enforcement, the technology serves as a tool to assist human analysts, who ultimately must use other means to verify an identity. The technology does not make a positive identification, establish probable cause for an arrest or otherwise make automated decisions.
Under traditional methods, police seek to identify an unknown person of interest during an investigation by manually looking through hundreds of mugshots with victims, canvassing areas with photos or searching a database using vague suspect descriptions or names that could easily be criminal aliases or fraudulent identities.
Facial recognition technology automates and improves the first step of identifying potentially matching photos from a database. Beyond improving an otherwise manual process, facial recognition contributes to more accurate identification. The National Institute of Science and Technology has found that forensic examiners performed best when supported by facial recognition technology and the most accurate performance resulted when these efforts are combined.
Proper use of this technology is critical to protecting the innocent, as eyewitness identifications in criminal investigations are notoriously prone to error. According to the Innocence Project, mistaken eyewitness identifications have been the key factor in 71 percent of wrongful convictions in the U.S. later overturned.
Three members of the LGBTQ+ community were shot and killed by a man at a local home in Detroit, Michigan, in 2019. The Detroit Police Department used facial recognition – in combination with other investigative tools – to help identify the suspect based on video images from a nearby gas station. The suspect was charged with three counts of murder, in addition to other charges.
Jarrod Ramos was angered by a story the Capital Gazette ran about him in 2011 and brought a lawsuit against the paper for defamation, which a judge later dismissed. In 2018, Ramos entered the newspaper’s headquarters in Annapolis, Maryland, with a shotgun and killed five employees, leaving two others critically injured. Anne Arundel County Police obtained an image of Ramos and sent it to the Maryland Combined Analysis Center, which helped identify him by comparing the photo to others in the Maryland Image Repository System.
A male victim was shot and seriously injured during a 2019 robbery in Detroit, Michigan. Using images from high-quality video surveillance covering the location, investigators aided by facial recognition software were able to identify the suspect, who was later taken into custody after further investigation.
In New York City in 2019, a man followed a young woman home from work and attempting to kidnap and rape her at knife point, after dragging her into a grassy area before eventually letter her go. Investigators used facial recognition technology to compare images from surveillance video at a nearby food store with a mugshot database. Along with additional investigative work, this allowed them to identify a suspect and make an arrest within 24 hours. The 27-year old suspect had previously been arrested for allegedly raping a 73-year old woman but was out on bail.
A 15-year old girl in Scranton, Pennsylvania, was sexually assaulted by an adult male she met online. Beyond seeing him in person, the only additional information she had was from his online profile. Police were able to use facial recognition on one of the digital images to provide some potential matches from a state database, from which the victim was able to identify a likely match. After additional investigative work, authorities obtained a search warrant for the home of the identified suspect, who later admitted to the crime.
In 2018, detectives in Munster, Indiana, tried to identify a suspect who had attempted to rob a local business at gunpoint, after releasing a photo from the location’s surveillance system which was shared by local media. No leads were generated until they used facial recognition and found possible match, a man that had skipped parole after serving a prison sentence for nine armed robberies in Illinois. The suspect was identified after the store owner was shown a photo lineup that included the man’s picture and was arrested several months later. Without facial recognition, the suspect would likely never have been found.
In 2017, a man accused of sexually assaulting a minor was apprehended in Oregon after a 16-year manhunt. Using facial recognition technology, the Federal Bureau of Investigation (FBI) was able to identify the suspect after a positive match was found when the suspect sought to acquire a U.S. passport. Similarly, in 2014, the FBI used facial recognition technology to help locate and apprehend a convicted pedophile who had been on the run for 14 years, returning him to New Mexico to face justice.
In 2020, police investigated the murder of a man in a Las Vegas, Nevada, Airbnb, that he had rented with his family. The police report indicated that the victim, who was shot to death in the bedroom of the Airbnb, owed the suspect $300 and some computer equipment. Witnesses who fled the scene with the gunman produced a Facebook profile of the suspect, which allowed officials to verify the true identity of the suspect using facial recognition as a secondary tool.
Las Vegas Metropolitan Police spokesman Aden Ocampo Gomez said “the technology is strictly a ‘secondary tool’ for investigators that has been in place for a couple of years. The database that police use relies on images of people previously arrested in Metro’s jurisdiction.”
Other cases in Las Vegas where suspects were identified with the help of facial recognition technology include:
- 22-year-old male charged with first-degree kidnapping, child abuse and sex trafficking
- 29-year-old male charged with kidnapping and sexually assaulting a 12-year-old girl; suspect was identified from surveillance video from a 7-Eleven
- 27-year-old male charged with sexual assault, burglary and other felonies involving an intoxicated woman at The Venetian
- 26-year-old male charged in a May 2019 shooting outside the Casa Grill and Bar on Maryland Parkway; one person was injured; police identified the suspect using surveillance video of the crime.
- 38-year-old male charged in January 2020 with battery resulting in substantial bodily harm; police said he punched a person at a car show, causing the victim to suffer a fractured skull; police used surveillance footage to identify the suspect
Preventing Identity Theft and Fraud
The Kansas Department of Revenue’s use of facial recognition software in 2014 led to the investigation of the largest forced labor trafficking case in the United States, all through identifying cases of driver’s license fraud in their database.
Identifying a Murderer in Las Vegas
Identifying and Arresting an Assailant
The New York City Police Department (NYPD) arrested a 31-year old man after he tried to rape and assault a woman on the subway at a station in midtown Manhattan. The NYPD facial recognition team utilized a video taken by a bystander to identify the man, based on a photo taken from a previous arrest.