NIST explores new tech for tattoo recognition
- By Derek Major
- Jun 10, 2015
While tattoos are often an outward expression of an essential part of a person’s character, to the participants of a challenge by the National Institute of Standards and Technology, body art can quite literally help confirm a person’s identity.
Tattoo forensic analysis is important to applications such as victim identification, crime solving and gang intelligence gathering, according to NIST. But tattoo recognition is difficult because the composition and patterns of the images vary widely. The current method of cataloging tattoos relies on a keyword-based process, which can be complex and subjective depending on the design of a tattoo and the description of the examiner.
The goal of the Tattoo Recognition Technology – Challenge (Tatt-C) is to advance research into automated image-based tattoo recognition technology, focusing on tattoo matching and retrieval from still images captured operationally by law enforcement agencies.
Workshop participants heard the results of a preliminary trial of existing tattoo recognition software in which the FBI Biometric Center of Excellence (BCOE) provided thousands of images to NIST, which then asked the six challenge participants to assess the capability of image-based tattoo recognition algorithms in the following operational use-cases:
- Visually similar or related tattoos from different subjects.
- Different instances of the same tattoo image from the same subject over time.
- A small region of interest that is contained in a larger image.
- Visually similar or related tattoos using different types of images such as sketches, scanned print, computer graphics or natural images.
- Whether an image contains a tattoo or not.
Tatt-C participants included Compass Technical Consulting; the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation; the French Alternative Energies and Atomic Energy Commission; MITRE; MorphoTrak and Purdue University.
NIST computer scientist Mei Ngan organized the challenge, and found that "the state-of-the-art algorithms fared quite well in detecting tattoos, finding different instances of the same tattoo from the same subject over time and finding a small part of a tattoo within a larger tattoo."
But two areas could use further research, according to Ngan: Detecting visually similar tattoos on different people and recognizing a tattoo image from a sketch or sources other than a photo.
"Improving the quality of tattoo images during collection is another area that may also improve recognition accuracy," Ngan said.
In addition to discussing the initial findings, participants also covered the use of image-based tattoo matching in operations, identified ways to improve tattoo recognition and discussed next steps NIST might take in this area.
Government researchers have been looking to develop automated tattoo recognition technology since 2012 when the BCOE issued a request for information on the best way to build a tattoo database.
Derek Major is a former reporter for GCN.