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Mobile fingerprint scanners making field IDs easier

The State of Georgia obtained a $1.2 million grant to buy 120 scanners

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The MorphoRapID handheld biometric terminals allow police to quickly carry out ID checks in the field.

Image Courtesy of Safran Morpho

Technology capable of scanning fingerprints in the field is becoming cheaper and more accessible. The most common use is for officers in the field to identify suspects and reveal aliases, but there are some other applications that are getting notice as well.

The product names vary only a little and can be confusing. Marketed by Crossmatch Technologies, the Mobile Rapid ID is a hand-held scanner that is about the same size and shape as a handheld metal detector. Another device, more commonly deployed in Europe and Australia, is the Morpho RapID, made by Sagem Morpho. The latter device is larger, resembling a smartphone with a scanning module attached to the top.

Mobile fingerprint scanners have a postage stamp-size platen for the fingertip. The information captured by the device isn’t so much a graphic representation of the print as a mathematical model of the relationships between the friction ridges or minutiae of the image. This allows the image to be transmitted as a string of numbers the Automated Fingerprint Identification System (AFIS) databases can use.

Typically, comparisons of transmitted fingerprint data are made with local databases, with the search expanding if a match isn’t found. The logic for this is simple enough. Most of your bad guys are already in your local database. Looking there first is faster, and saves bandwidth and time in what may be unnecessary searches of the larger files.

When a match is found, the information associated with that match (name, date of birth, physical description, possibly a mug shot) is returned to the device that sent the inquiry. Because the match is made with only partial fingerprint data, it’s always possible to have the data match more than one individual and to get a “match” of someone other than the person whose finger was scanned. Most of the time, comparison of the “match” data with the characteristics individual at hand clears up any misidentification. If age, race, height and weight are considerably off from the individual at the scene, you probably have a mismatch. Definitive identification of someone by fingerprints must always be done by a human print examiner, even when prints from all ten fingers are available.

In the field, the data returned by mobile scanners is usually sufficient for the mission. When suspects provide false names to officers, the mobile scanner reveals them for who they are if they’re already in the system, and the people officers are most interested in identifying are. These include sex offenders, wanted persons, gang members, and people who are the subject of protective or restraining orders. More often than not, when confronted with their true names, individuals who have provided false names will own up.

The technology is also useful in jails and prisons. Although inmates are usually issued identifying wristbands or ID cards, they will switch these to gain access to areas normally denied to them, or to be released under the name of another inmate. When correctional officers have access to a mobile scanner, any mistaken identity issues are cleared up immediately.

The State of Georgia obtained a $1.2 million grant to purchase 120 Rapid ID scanners for use by law enforcement agencies in and around the Atlanta area. The scanners cost between $2,000 and $2,800 each, depending on configuration. A story in the Atlanta Journal-Constitution reported that, out of 13,589 checks performed with the mobile scanners, 6,887 or just over 50 percent resulted in hits for people already in the system.

The American Civil Liberties Union (ACLU) has voiced some concerns about privacy. They say that the use of these scanners may allow police to collect the fingerprints of people who have not been charged with a crime. Mobile ID scanners do not store or record the fingerprints of people using the device. The information captured by the devices is compared against the AFIS database, then discarded if there are no hits.


Learn more about Crossmatch Technologies.

Learn more about Sagem Morpho.

Tim Dees is a writer, editor, trainer and former law enforcement officer. After 15 years as a police officer with the Reno Police Department and elsewhere in northern Nevada, Tim taught criminal justice as a full-time professor and instructor at colleges in Wisconsin, West Virginia, Georgia and Oregon. He was also a regional training coordinator for the Oregon Dept. of Public Safety Standards & Training, providing in-service training to 65 criminal justice agencies in central and eastern Oregon.
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