Unsupervised tattoo segmentation combining bottom-up and top-down cues

Allen, Josef D.; Zhao, Nan; Yuan, Jiangbo; Liu, Xiuwen

doi:10.1117/12.884368

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…The quantization error problem in the BOW model was also addressed in [8] using Hamming distance and geometry consistency to improve retrieval accuracy. In [9,10,11] efficient tattoo segmentation methods were introduced. A tattoo segmentation and retrieval method when the tattoo image was not pre-cropped is described in [10].…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Robust local and global shape context for tattoo image matching

Kim¹,

Parra²,

Yue³

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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Tattoos can provide useful information related to criminal gang activity. Law enforcement can use the information embedded in tattoos to identify and track the criminal history of a suspect. For matching processes, tattoo images are difficult to use due to problems such as deformations and weak edge structures. In this paper we describe a tattoo image retrieval and matching system based on a combination of local and global image matching methods to improve matching accuracy. The proposed local shape context combined with SIFT descriptors are used for local features of a tattoo object and global shape is used for overall shape of a tattoo object. The contributions of this paper include the introduction of a multiple different sizedbin polar histograms based local shape context (MHLC) and a global shape descriptor combining the multiple different sized-bin polar histogram and 2D Fourier Transform for robustness of translation, scale, rotation and shape distortions. We also describe robust similarity for local descriptors and a weighted matching method based on local and global descriptors. Our experimental results show that our proposed method performs better than previously published tattoo image retrieval systems.

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Section: Review Of Existing Methodsmentioning

confidence: 99%

Robust local and global shape context for tattoo image matching

Kim¹,

Parra²,

Yue³

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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“…A tattoo is an elective biometric trait that contains discriminative information to support person identification and investigation in addition to traditional soft biometrics such as age, gender and race. While some research has been done in the area of image-based tattoo detection and retrieval [9,10,13,16,19,20], it is not a mature domain. Prior to this study, there were no common datasets to evaluate and develop operationally-relevant tattoo recognition applications.…”

Section: Executive Summary Backgroundmentioning

confidence: 99%

Tattoo Recognition Technology -- Challenge (Tatt-C) Outcomes and Recommendations

Ngan¹,

Quinn²,

Grother³

2015

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Tatt-C was conducted as an "open-book" test, where participants were provided with the dataset and ground-truth data, ran their algorithm(s) on the data following a specified protocol on their own hardware, and provided their system output to NIST for uniform scoring and analysis. Accuracy was measured for the five Tatt-C use cases, including the impact of gallery size for certain scenarios. Detailed descriptions and image examples of the use cases can be found in Section 2.2 of this report. Key Results Key results for the five use cases studied are: • Tattoo Identification evaluated matching different instances of the same tattoo image from the same subject over time. On a gallery size of 4 375, the top performing algorithm (MorphoTrak) reported a rank 10 hit rate ⇤ of 99.4% and mean average precision (MAP) ⇤ of 99.4%. Section 3.1 • Region of Interest evaluated matching a subregion of interest that is contained in a larger image canvas. On a gallery size of 4 363, the top performing algorithm (MorphoTrak) reported a rank 10 hit rate of 97% and MAP of 95.4%. Section 3.2 • Mixed Media evaluated matching visually similar or related tattoos using different types of non-tattoo imagery (i.e. sketches, scanned print, computer graphics, and graffiti). On a gallery size of 55, the top performing algorithm (MITRE) reported a rank 10 hit rate of 36.5% and MAP of 15.1%. Section 3.3 • Tattoo Similarity evaluated matching visually similar or related tattoos from different subjects. On a gallery size of 272, the top performing algorithm (MITRE) achieves a rank 10 accuracy of 14.9% and MAP of 5.2%. Section 3.4 • Tattoo Detection evaluated detecting whether an image contains a tattoo or not. On a mixed dataset of 1 349 tattoo images and 1 000 face images, the top performing algorithm (MorphoTrak) reported an overall detection accuracy ⇤ of 96.3%. Section 3.5 Factors that influenced accuracy included: • Algorithms: Tattoo detection and matching accuracy depends strongly on the implementation of the core technology as algorithm performance varied substantially. Sections 3.1, 3.2, 3.3, 3.4, and 3.5 ⇤ For the definition of hit rate, MAP, and overall detection accuracy, see Section 2.3. Generally speaking, the higher the hit rate, MAP, and detection accuracy value, the more accurate the algorithm. Finally, the authors are grateful to Amanda Noxon (Michigan State Police), Dr. Jim Matey (NIST), and Mike Garris (NIST) for their thorough and constructive review of this document. Release Notes. Versioning: This document is Revision 1.0 of the original report, which was originally published in September 2015.. Typesetting: Virtually all of the tabulated content in this report was produced automatically. This involved the use of scripting tools to generate directly type-settable L A T E X content. This improves timeliness, flexibility, maintainability, and reduces transcription errors.. Graphics: Many of the figures in this report were produced using Hadley Wickham's ggplot2 [29] package run ning under , the capabilities of which extend bey...

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“…[6] also published results on a dataset of 256 tattoo images collected from the Internet, and [8] evaluated on a dataset of 18,922 images collected from the Internet and sampling from public face databases. [11] tested against 69,507 operational tattoo images extracted from the Michigan State Police Tattoo Database, which is not available for public use.…”

Section: Existing Databasesmentioning

confidence: 99%

“…While some research has been done in the area of imagebased tattoo recognition [4,5,6,7,8,9], it is not a mature industry. There is no common research data and use cases to evaluate and develop systems for next generation government applications.…”

Section: Introductionmentioning

confidence: 99%

Tattoo recognition technology - challenge (Tatt-C): an open tattoo database for developing tattoo recognition research

Ngan

Grother

2015

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)

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Tattoos have been used for many years to assist law enforcement in investigations leading to the identification of criminals and victims. A tattoo is an elective biometric trait that could contain more discriminative information to support person identification than traditional soft biometrics such as age, gender and race. While some research has been done in the area of image-based tattoo detection and retrieval, it is not a mature domain. There are no common datasets to evaluate and develop operationally-relevant tattoo recognition applications. To address this shortcoming, the NIST Tattoo Recognition Technology Challenge (Tatt-C) database was developed as an initial tattoo research corpus that addresses use cases representative of operational scenarios.The Tatt-C database represents an initial attempt to provide a set of ground-truthed tattoo images focused on, but not limited to, five primary use cases. This paper describes the details of the database along with the experimental protocols and test cases that should be followed, which will enable consistent performance comparison of tattoo recognition methods.

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Unsupervised tattoo segmentation combining bottom-up and top-down cues

Cited by 14 publications

References 19 publications

Robust local and global shape context for tattoo image matching

Robust local and global shape context for tattoo image matching

Tattoo Recognition Technology -- Challenge (Tatt-C) Outcomes and Recommendations

Tattoo recognition technology - challenge (Tatt-C): an open tattoo database for developing tattoo recognition research

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