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MorphoTrak tattoo recognition algorithm places first in NIST evaluation

Categories Biometrics News  |  Trade Notes

MorphoTrak announced that its tattoo recognition algorithm placed first in National Institute of Standards and Technology’s (NIST) “Tattoo Recognition Technology – Challenge (Tatt-C)” evaluation.

Each NIST Tatt-C trial examined a crucial element of performance for an automated tattoo recognition solution.

In the identification trials, the MorphoTrak algorithm was able to successfully identify different instances of the same tattoo on the same subject, collected over time.

Additionally, MorphoTrak was successful in finding a small concentrated area within a larger tattoo, as well as determining whether an image featured a tattoo.

Tattoo images have long been considered a soft biometric, or in other words, visual data that can be used to better determine the scope of candidates for identification and investigation, but cannot be used to accurately identify a person.

Though law enforcement organizations are able to submit mugshots for automated searches using face recognition algorithms, tattoos are still limited to being grouped by text, in broad categories such as “Dragon,” and “Skull.”

MorphoTrak developed the tattoo recognition algorithm as a means to help law enforcement move away from keyword search and adopt a more efficient and automated search of tattoo images, similar to searching fingerprints and faces.

“MorphoTrak is proud to continue its tradition of leadership and commitment to excellence in the field of biometric technology,” said Celeste Thomasson, president and CEO of MorphoTrak. “Prior to MorphoTrak’s work in this area, investigators had to rely on text keywords to find tattoos that were similar in appearance. Our continuously improving tattoo recognition algorithm takes the criminal justice, forensic investigation and public security communities one step closer to a high-performance automated tattoo recognition solution.”

Previously reported, MorphoTrak will begin offering vendor-independent training in face comparison, in which it will instruct individuals about computer-aided face recognition and facial identification.

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