1
Tattoo Recognition Technology (Ta t t ) Program
Ta t t -C
“Open-book” challenge with
public dataset to engage research
community to advance image-
based tattoo matching technology
Ta t t -BP
Best practice guidelines and
training material for the collection
of tattoo images
Ta t t -E
Large-scale sequestered
evaluation of tattoo recognition
algorithms
2014 - 2015
2015 - 2016
2016 - 2017
“Open-book” test with public
dataset
Best Practice Documents Sequestered Test
Future Activities
TBD
TBD
Comments and
discussion welcome via
Figure 1: Activities under the Tattoo Recognition Technology Program, including planned future projects. See website http://www.
nist.gov/itl/iad/ig/tattoo.cfm for latest project status.
1 Introduction
This document, along with a supplementary poster [3] and a set of instructional slides [2], provides best practice guidelines
for the collection of good quality tattoo images. As an outcome of the Tatt-C 2015 [4] activity, algorithm failure to correctly
match a tattoo is often related to the consistency and quality of image capture. Notably, inconsistencies in image angle,
orientation, size of the tattoo relative to the entire image and poor collection characteristics such as poor illumination,
low contrast, blur, and the existence of clothing and background clutter caused failures for tattoo detection and matching
algorithms. While some problems can potentially be rectified via post-capture image processing, which imposes additional
human labor, certain properties cannot be recovered after the photograph is taken. As such, certain guidelines should
be followed to ensure good quality images are collected. For the purpose of this document, for specific photographic
guidance that is not detailed in this document, the recommendations of ANSI/NIST-ITL Standard [1] Annex E (Facial
Capture) apply.
1.1 Audience
The intended audience of this document is law enforcement professionals or officers who photograph tattoos and/or
design or specify image collection processes. The recommendations provided are simple and straightforward. Although
the number of ways image collection can go wrong might appear quite numerous, a single adjustment can often rectify
several problems at once. Thus, the operator only needs to remember a few simple guidelines to deal with the majority of
problems that might occur.
2 The Tattoo Recognition Technology Program
The Tattoo Recognition Technology Program (Figure 1) was initiated by NIST to support an operational need for image-
based tattoo recognition and classification to support law enforcement and forensic applications. The program provides
quantitative support for tattoo recognition development and best practice guidelines. A summary of past, ongoing, and
planned activities is provided below.
• Tatt-C [4] was an initial research challenge that provided operational data and use cases to engage the research com-
munity into advancing research and development into automated image-based tattoo technologies. It also sought
to assess the state-of-the-art to determine what methods are effective and viable for pertinent operational scenarios.
NIST hosted a culminating industry workshop and published a public report on the outcomes and recommenda-
tions from the Tatt-C activity. While the Tatt-C participation period has closed, the Tatt-C dataset is available to
TATT-BP: GUIDELINES FOR TATTOO IMAGE COLLECTION