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Tattoo Recognition Technology - Evaluation (Tatt-E): Performance of Tattoo Identification Algorithms



Mei L. Ngan, Patrick J. Grother, Kayee K. Hanaoka


NIST performed a large scale empirical evaluation of tattoo recognition algorithms. The test leveraged large operational datasets comprised of tattoo images from law enforcement databases, enabling evaluation with enrollment database sizes of up to 100,000. NIST employed a lights-out, black-box testing methodology designed to model operational reality where software is shipped and used as-is, without algorithmic training. Core tattoo identification accuracy was baselined over tattoo images used as-is, then traded off against gallery size and search speed. The effects of cropping around the primary tattoo content, skintone, contrast, and tattoo-to-image ratio were assessed, and matching accuracy on sketch images and tattoos collected in the short-wave infrared (SWIR) spectrum are also reported. In addition, performance on algorithmic capability to do tattoo detection and tattoo localization as separate tasks are also documented.
NIST Interagency/Internal Report (NISTIR) - 8232
Report Number


tattoo recognition, biometrics


Ngan, M. , Grother, P. and Hanaoka, K. (2018), Tattoo Recognition Technology - Evaluation (Tatt-E): Performance of Tattoo Identification Algorithms, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], (Accessed May 27, 2024)


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Created October 31, 2018, Updated November 10, 2018