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Tattoo Recognition Technology – Challenge (Tatt-C)

Scope

The Tattoo Recognition Technology – Challenge (Tatt-C) is being conducted to challenge the commercial and academic community in advancing research and development into automated image-based tattoo matching technology.  The activity will assess the capability of image-based tattoo recognition algorithms to perform detection and retrieval of tattoos, with the goals to determine which algorithms are most effective and whether any are viable for the following operational use-cases: 1. Tattoo Similarity – matching visually similar or related tattoos from different subjects; 2. Tattoo Identification -  matching different instances of the same tattoo image from the same subject over time; 3. Region of Interest -  matching a small region of interest that is contained in a larger image; 4. Mixed Media - matching visually similar or related tattoos using different types of images (e.g.sketches, scanned print, computer graphics, or natural images); 5. Tattoo Detection -  detecting whether an image contains a tattoo or not.

Interested Parties

Please contact NIST if:

a)  You are a developer of tattoo matching algorithms or have an interest in developing such a capability.

b)  You represent an organization possessing suitable tattoo datasets that might be valuable to our effort.

c)  You have an operational interest or need for image-based matching of tattoo images.

Background

Tattoos have been used for many years to assist law enforcement in the identification of criminals and victims and for investigative research purposes. Tattoos provide valuable information on an individual’s affiliations or beliefs and can support identity verification of an individual. Historically, law enforcement agencies have followed the ANSI-NIST-ITL 1-2011 standard to collect and assign keyword labels to tattoos. This keyword labeling approach comes with drawbacks, which include the limitation of ANSI-NIST standard class labels to describe the increasing variety of new tattoo designs, the need for multiple keywords to sufficiently describe some tattoos, and subjectivity in human annotation as the same tattoo can be labeled differently between examiners. As such, the shortcomings of keyword-based tattoo image retrieval have driven the need for automated image-based tattoo recognition capabilities.

Structure of Tatt-C

Tatt-C is structured around problems that are designed to challenge the commercial and academic community in advancing research and development into automated image-based tattoo recognition technology. While some research and commercial capability is available, tattoo recognition is not a mature industry. There is no common test data and use cases to evaluate and develop systems for next generation government applications. To address this shortcoming, the Tatt-C dataset was developed as an initial tattoo test corpus that addresses use cases derived from operational scenarios provided by the FBI’s Biometric Center of Excellence (BCOE).

The Tatt-C dataset consists of still images of tattoos captured operationally by law enforcement agencies. The operational nature of this corpus imposes challenges on traditional image retrieval methodologies given the large variation in capture environment/process and tattoo content/quality. The following are examples of such challenges represented in the Tatt-C dataset:

  • Inconsistent image lighting and scale
  • Occlusions due to clothing and different image backgrounds
  • Different tattoo background or embellishments around primary tattoo content
  • Blended images or multiple tattoos in a single image
  • Inconsistent orientation of body/appendages and images
  • Extremely faded tattoos
  • Ambiguous or unfamiliar abstractions (difficult or impossible for different people to view and interpret consistently)

The Tatt-C dataset provides a basis for objectively measuring and comparing tattoo recognition capabilities, with partitions focused on but not limited to the following use cases:

  • Tattoo Similarity. What is the retrieval performance for finding visually similar or related tattoos from different subjects?
  • Tattoo Identification. What is the retrieval performance for finding different instances of the same tattoo image from the same subject over time?
  • Region of Interest. What is the retrieval performance for finding a small region of interest that is contained in a larger image?
  • Mixed Media. What is the retrieval performance for finding visually similar or related tattoos using different types of images (e.g. sketches, scanned print, computer graphics, or natural images)?
  • Tattoo Detection. What is the performance for detecting whether an image contains a tattoo or not?

Participation in Tatt-C

At a high level, participants of the challenge would obtain the dataset, run their algorithms against the specified partitions, and send their results back to NIST.  The Tatt-C participation window will open Fall 2014.  Instructions for obtaining the dataset will be posted at that point in time.

Contact Information:

For more information, please contact tattoo AT nist DOT gov.

Past and current work on image-based tattoo recognition

The following is a working list of publications related to tattoo matching and retrieval methods.

A. K. Jain, J.-E. Lee, and R. Jin, "Tattoo-id: automatic tattoo image retrieval for suspect and victim identification," Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing, PCM’07, pp. 256–265, Springer-Verlag, Berlin, Heidelberg, 2007. http://www.cse.msu.edu/biometrics/Publications/SoftBiometrics/JainLeeJinTattoo07.pdf

A. K. Jain, Y. Chen and U. Park, "Scars, Marks and Tattoos: Physical Attributes for Person Identification," MSU Tech Report CSE 07-22, 2007.  http://www.cse.msu.edu/publications/tech/TR/MSU-CSE-07-22.ps

S. T. Acton and A. Rossi, "Matching and retrieval of tattoo images: Active contour cbir and glocal image features," Proc. of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 21–24, IEEE Computer Society, Washington, DC, USA, 2008. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4512275

J.-E. Lee, A. K. Jain, and R. Jin, "Scars, marks and tattoos (SMT): Soft biometric for suspect and victim identification," Proceedings of the Biometrics Symposium, pp. 1–8, September 2008, Tampa, Florida, USA. http://www.cse.msu.edu/biometrics/Publications/SoftBiometrics/LeeJainJin_SMT_ BSYM2008.pdf

A. K. Jain, J.-E. Lee, R. Jin, and N. Gregg, "Content-based image retrieval: An application to tattoo images," Proceedings of the International Conference on Image Processing, pp. 2745–2748, 2009. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5414140

J. D. Allen, N. Zhao, J. Yuan, and X. Liu "Unsupervised tattoo segmentation combining bottom-up and top-down cues", Proceedings of the SPIE 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011, 80630L (May 31, 2011).

http://ww2.cs.fsu.edu/~jyuan/papers/tattoo_2011.pdf

D. Manger, "Large-scale tattoo image retrieval," Proceedings of the Conference on Computer and Robot Vision, pp. 454–459, May 2012, Toronto, Ontario, Canada.

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6233176

J.-E. Lee, R. Jin, A. K. Jain, and W. Tong, "Image retrieval in forensics: Tattoo image database application," IEEE MultiMedia, vol. 19, no. 1, pp. 40–49, 2012.

http://www.cse.msu.edu/biometrics/Publications/SoftBiometrics/LeeTongJinJain_Image RetrievalForensics_ApplicationTattooImageDatabase_IEEEMultimedia11.pdf

B. Heflin, W. J. Scheirer, T. E. Boult, "Detecting and Classifying Scars, Marks, and Tattoos Found in the Wild," Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems, 2012.

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6374555

H. Han, A. K. Jain, "Tattoo based identification: Sketch to image matching", Proceedings of the International Conference on Biometrics Compendium, pp. 1-8, 2013. http://www.cse.msu.edu/biometrics/Publications/SoftBiometrics/HanJain _TattooBasedIdentification_Sketch2ImageMatching_ICB13.pdf

P. Duangphasuk and W. Kurutach, "Tattoo skin detection and segmentation using image negative method," Proceedings of the 13th International Symposium on Communications and Information Technologies, pp. 354-359, 2013.

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6645881

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