Image Retrieval in Forensics: Tattoo Image Database Application

Lee, Jung-Eun; Jin, Rong; Jain, Anil K.; Tong, Wei

doi:10.1109/mmul.2011.59

Cited by 63 publications

(54 citation statements)

References 12 publications

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“…Hence, the proposed tattoo sketch to image matching method is evaluated by examining the top-200 retrieval rate using a Cumulative Match Characteristic (CMC) curve. The accuracy of the proposed method is compared against a state-of-the-art image-to-image tattoo matching system, Tattoo-ID [16,20].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…A state-of-the-art image-to-image tattoo matcher, called Tattoo-ID [20] is used as the baseline. As shown in Fig.…”

Section: Matching Performancementioning

confidence: 99%

“…For example, law enforcement agencies usually follow the ANSI/NIST-ITL1-2011 standard 5 for assigning a single keyword to a tattoo image in the database. However, a keyword-based tattoo image retrieval has several limitations in practice [20]: (i) The ANSI/NIST classes define a limited vocabulary which is insufficient for describing various tattoo patterns; (ii) multiple keywords may be needed to adequately describe a tattoo image; (iii) human annotation is subjective and different subjects can give dramatically different labels to the same tattoo.…”

Section: Introductionmentioning

confidence: 99%

“…Lee et al [20] improved the performance of the Tattoo-ID system by developing a more robust similarity measures and incorporating the metadata associated with tattoo images. Heflin et al [13] proposed a method to detect and classify scars, marks and tattoos under unconstrained conditions, and adapted Tattoo-ID system to a scenario of open set classification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tattoo based identification: Sketch to image matching

Han

Jain

2013

2013 International Conference on Biometrics (ICB)

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Section: Evaluation Metricsmentioning

confidence: 99%

“…A state-of-the-art image-to-image tattoo matcher, called Tattoo-ID [20] is used as the baseline. As shown in Fig.…”

Section: Matching Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tattoo based identification: Sketch to image matching

Han

Jain

2013

2013 International Conference on Biometrics (ICB)

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“…For instance, CBIR approaches are typically concerned with broad concepts (i.e. find all pictures of cows by a lake), and make use of other meta-data such as captions to learn the relationship between the image and the underlying content [84,92]. By contrast, when locating a person, a more specific and inter-related set of concepts is used, such as a tall male with a plain red shirt and black trousers.…”

Section: Semantic Searchmentioning

confidence: 99%

Locating People in Video Surveillance from Semantic Descriptions

Halstead¹

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AcknowledgmentsThe completion of my PhD research programme has been an enjoyable and at times difficult and complex journey that has involved a number of people that have assisted me in so many ways throughout the process. The people that I thank here is by no means a comprehensive list as there isn't enough paper for the names I have to mention.I would firstly like to thank my supervisory team, Clinton Fookes, Sridha Sridharan, and Simon Denman for providing valuable guidance throughout the course of my study. I would like to thank Clinton and Sridha for providing me with the opportunity to study my PhD, and also for providing the Vision and Signal Processing (VSP) laboratory environment that allowed me to research at a high level, and interact with other PhD students to assist with my PhD. I would like to make special mention of Simon for his day to day support and being consistently available to help me with my endless questions, and without his help, guidance, and encouragement I would not have made it to the end of my PhD. Another member of the of the VSP discipline that I would like to thank is David Dean, as a member of VSP he assisted me multiple times throughout the course of my PhD.I would also like to thank the other students and past and present staff within the VSP laboratory, thank you for the sharing of knowledge but mostly thank you all for the entertainment, and enjoyable moments that we shared throughout this time. AbstractIn surveillance and security situations it is often necessary to search and locate a subject of interest from a verbally or textually supplied subject description. At present, searches using these forms of queries are still predominantly performed manually and often ineffectively, via physical searching of a premises or personally watching countless hours of video footage, looking for the specific subject of interest. Surveillance networks are typically monitored by few people viewing several monitors displaying single and multiple camera feeds. In these situations, the ability to translate an externally supplied description into one of computer vision relevance would be of great assistance.To date, state-of-the-art soft biometric based search techniques primarily include some form of pre-enrollment component, where the target subject is enrolled from one camera and searched in other cameras for a match, or alternatively enrolled in an earlier instance of the same camera, and searched for in later footage. These re-identification techniques exclude however, instances where the subject has not previously been located and enrolled from the footage.Soft biometric based semantic search is one technique that provides a solution to this problem. The target query no longer requires scene interrogation and instead can be generated from a textually supplied query. While soft biometrics have obvious limitations, non-permanence and not individually identifiable, they are generally simplistic for a human operator to visually extract and relay to another human operator. They also have th...

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On Designing a Forensic Toolkit for Rapid Detection of Factors that Impact Face Recognition Performance When Processing Large Scale Face Datasets

Rose

Bourlai

2020

Advanced Sciences and Technologies for Security Applications

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Image Retrieval in Forensics: Tattoo Image Database Application

Cited by 63 publications

References 12 publications

Tattoo based identification: Sketch to image matching

Tattoo based identification: Sketch to image matching

Locating People in Video Surveillance from Semantic Descriptions

On Designing a Forensic Toolkit for Rapid Detection of Factors that Impact Face Recognition Performance When Processing Large Scale Face Datasets

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