On Designing Tattoo Registration and Matching Approaches in the Visible and SWIR Bands

Face, iris and fingerprint based biometric systems are well explored areas of research. However, there are law enforcement and military applications where neither of the aforementioned modalities may be available to be exploited for human identification. In such applications, soft biometrics may be the only clue available that can be used for identification or verification purposes. Tattoo is an example of such a soft biometric trait. Unlike face-based biometric systems that used in both same-spectral and cross-spectral matching scenarios, tattoo-based human identification is still a not fully explored area of research. At this point in time there are no pre-processing, feature extraction and matching algorithms using tattoo images captured at multiple bands. This thesis is focused on exploring solutions on two main challenging problems. The first one is cross-spectral tattoo matching. The proposed algorithmic approach is using as an input raw Short-Wave Infrared (SWIR) band tattoo images and matches them successfully against their visible band counterparts. The SWIR tattoo images are captured at 1100 nm, 1200 nm, 1300 nm, 1400 nm and 1500 nm. After an empirical study where multiple photometric normalization techniques were used to pre-process the original multi-band tattoo images, only one was determined to significantly improve cross spectral tattoo matching performance. The second challenging problem was to develop a fully automatic visible-based tattoo image registration system based on SIFT descriptors and the RANSAC algorithm with a homography model. The proposed automated registration approach significantly improves the operational cost of a tattoo image identification system (using large scale tattoo image datasets), where the alignment of a pair of tattoo images by system operators needs to be performed manually. At the same time, tattoo matching accuracy is also improved (before vs. after automated alignment) by 45.87% for the NIST-Tatt-C database and 12.65% for the WVU-Tatt database

Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain

Aplicação de redes neurais convolucionais na identificação de tatuadores

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.As tatuagens são uma forma de modificação corporal baseada na inserção de tintas na pele que alteram a sua coloração. Os procedimentos de tatuagens têm sido realizados a milhares de anos com os primeiros registros de múmia tatuadas datando do quarto milênio a.C.. Hoje, a tatuagem tem a função de auto identificação. As pessoas buscam tatuagens com o objetivo de expressar sua identidade interior. Logo, há uma relação de unicidade em cada tatuagem. Além disso, a tatuagem recebeu o status de arte, sendo o tatuador considerado um pintor que utiliza a pele humana como meio de expressão. O crescimento das mídias sociais aumentou o compartilhamento de imagens de tatuagens e consequentemente o seu plágio, comprometendo o tatuado e o tatuador. Este trabalho propõe a utilização de rede neurais convolucionais para a identificação da autoria de tatuagens com o objetivo de proteger o artista. O sistema proposto classifica as imagens a partir das características extraídas das imagens de trabalhos passados de sete artistas. No desenvolvimento desse sistema, criou-se um banco de dados contendo os trabalhos de diferentes tatuadores e o teste de cinco diferentes arquiteturas de classificação. O banco de dados contem mais de 1.800 imagens de 7 diferentes tatuadores de 4 diferentes estilos. Fez-se o uso de Redes Neurais Convolucionais, uma técnica de Aprendizado de Máquina, para a extração das características e classificação das imagens. Utilizou-se uma rede já treinada para a extração das características básicas e adicionaram-se camadas a essa rede que foi treinada para realizar a classificação. A rede se mostrou capaz de realizar corretamente a identificação de cada autor. A arquitetura com o melhor desempenho classificou corretamente, em média, 83,7% das imagens dos sete artistas e teve um F1score total de 83. Assim, conclui-se que o procedimento se mostrou eficaz neste contexto.Tattoos are a form of body modification caracterised by the insertion of ink into the skin. Tattoing procedures have been done for thousands of years with the evidences of tattooed mummies from the fourth millenium BC. Recently, tattoo has received the status of art and tattoo artists are considered painters that use skin as a canvas. But, with the growth of image sharing in social media, the number of cases of tattoo plagiarismhas also grown, which affects both the tattoo owner and the tattoo artist. This project proposes a technique to identify the authorship of tattoos aiming to protect the tattoo artist. This system classifies imagens of seven selected tattoo artists using a set of features extracted from previous works of each artist. During the development of this project, a dataset of tattoo images was created and five different classifying algorithms were tested. The dataset has more than 1,800 images of 7 artist with 4 different styles. Convolutional neural networks were used for the features extraction and classification of the images. A pretrained network was used as the feature extractor. Extra layers were added to the pre-trained network for the classification task. The network was successfully identified the artists. The best performing architecture correctly classified on average 83.7% of the total images and had an overall F1score of 83. Thus, we conclude that the procedure proved to be effective in this context

The Forensic Analysis of Skin-Safe Stamp Pad Inks

Skin-safe stamp pad inks are currently being used at locations such as clubs, zoos and other events with the purpose of stamping individuals that visit the location. This is done in an attempt to monitor the number and activity of visitors. Ink manufacturing companies generally tend to experiment with a number of different combinations of the different components of ink, in order to obtain desirable properties. The combinations are usually proprietary and that very nature of the inks ends up making them, possibly unique and individual. The analysis, identification and possible individualization of the chemical composition of these inks could play a significant role in providing crucial evidential information to investigators in forensic cases. The aim of this research was to scientifically evaluate these inks by documenting the physical properties of the inks both macroscopically and microscopically and by identifying the optical and chemical properties of the ink components spectroscopically. The application of Ultraviolet-Visible (UV-VIS) Spectroscopy and Fourier-transform Infrared Spectroscopy (FT-IR) to the analysis of skin safe stamp pad inks sold for temporary tattooing purposes, has been explored in this project. Results from this study indicated that each step of the analysis and each technique used by itself, was powerful to get us closer and closer to a full discrimination of the ink samples and by combining the steps, all the analyzed samples were successfully discriminated. The results from this research can be used as reference spectra for potential skin-safe stamp pad ink forensic evidence. Furthermore, the use of the Bio-Rad software to make positive correlations with the reference samples listed in the software’s extensive database acted as a key first step towards possibly determining the identity of iii the components in the ink samples. Additional analysis using other techniques such as X-Ray fluorescence or Raman Spectroscopy could reveal more information about the different components of skin-safe inks

Learning about Large Scale Image Search: Lessons from Global Scale Hotel Recognition to Fight Sex Trafficking

Hotel recognition is a sub-domain of scene recognition that involves determining what hotel is seen in a photograph taken in a hotel. The hotel recognition task is a challenging computer vision task due to the properties of hotel rooms, including low visual similarity between rooms in the same hotel and high visual similarity between rooms in different hotels, particularly those from the same chain. Building accurate approaches for hotel recognition is important to investigations of human trafficking. Images of human trafficking victims are often shared by traffickers among criminal networks and posted in online advertisements. These images are often taken in hotels. Using hotel recognition approaches to determine the hotel a victim was photographed in can assist in investigations and prosecutions of human traffickers. In this dissertation, I present an application for the ongoing capture of hotel imagery by the public, a large-scale curated dataset of hotel room imagery, deep learning approaches to hotel recognition based on this imagery, a visualization approach that provides insight into what networks trained on image similarity are learning, and an approach to image search focused on specific objects in scenes. Taken together, these contributions have resulted in a first in the world system that offers a solution to answering the question, `What hotel was this photograph taken in?\u27 at a global scale

Morgan Kane: A Nordic Western

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CAFF Monitoring Series Report No. 1 - Development of a pan-Arctic monitoring plan for polar bears: background paper

CAFF Monitoring Series Report No. 1 - Development of a pan-Arctic monitoring plan for polar bears: background paper. Circumpolar Biodiversity Monitoring Programme,CAF

Frequency of significant steatosis in various chronic liver diseases: an evaluation with Transient Elastography (TE)

INTRODUCTION: TE was developed as a non-invasive method to assess liver fibrosis and steatosis using shear wave velocity. Many studies have proven its’ effectiveness as a method for evaluating liver fibrosis and steatosis.1-2 OBJECTIVE: To determine the prevalence and aetiology of steatosis in our local population. METHOD: This study was conducted as a retrospective review on all patients who had TE performed at UMMC from 1 January 2013 to 31 December 2021. Their demographics, clinical characteristics and TE findings were charted. RESULTS: A total of 3066 patients were included. 51.7% were males and 48.3% were females. The median CAP value was 271 dB/m. The median E value was 6.5kPa. 61.2% of patients had steatosis, with a staggering number of of these patients having significant steatosis (51.8%). 6.3% of patients had S2 steatosis whereas 45.5% of patients had severe (S3) steatosis. Interestingly, in those with S2 steatosis, 34.7% had chronic hepatitis B (CHB), 31.5% had non-alcoholic fatty liver disease (NAFLD), 5.2% with chronic hepatitis C (CHC) and 1% had alcoholic liver disease (ALD). In the S3 steatosis group, 66.7% had NAFLD, followed by ALD (36.6%), CHB (30.1%) and CHC (27.7%). 221 DISCUSSION: It is important to highlight that a large proportion of our patients has significant steatosis. This is likely in keeping with the global rise of obesity and sedentary lifestyle.3 NAFLD is a 4-decades old nomenclature that does not appropriately address the heterogenous pathogenicity of fatty liver disease. Our study reflects this heterogeneity, as it shows that steatosis often co-exists with other diverse aetiologies. CONCLUSION: Whilst NAFLD clearly has the greatest frequency of severe steatosis, it is also present in other aetiologies. These findings support the new terminology of metabolic associated fatty liver disease (MAFLD), which reflects the fact that NAFLD commonly co-exists with other aetiologies

Monitoring HIV-1 Group M in the Asia-Pacific

The Asia-Pacific is home to more than 60% of the world‘s inhabitants and the region second hardest hit by the effects of the HIV-1 pandemic, next to Sub-Saharan Africa. Many countries are low- or middle-income economies where a limited number of standardised antiretroviral therapies (ARTs) are available to treat HIV-infected patients. In resource-limited settings, viral load (VL) monitoring is not generally available to evaluate the effects of treatment. Furthermore, information is lacking as to the appropriate frequencies of VL monitoring to partner with ART. With suboptimal VL monitoring, virological breakthrough may be detected late, facilitating the accumulation of drug resistance-associated mutations (RAMs). Transmitted drug resistance would threaten already limited treatment options in the region. Viral diversity in HIV-1 epidemics is increasing. Predominant regional genotypes are subtypes B and C, CRF01_AE and their recombinants.Objectives were to contribute to efforts to monitor regional HIV-1. I evaluated impacts of diagnostic resourcing on patient treatment outcomes and provided estimates of transmitted HIV drug resistance (HIVDR). Associations between sexual exposures and HIV-1 genotype were evaluated, as were relationships between genotype and patient treatment outcomes.Analysis outcomes spanned the years 2000–2010 and included patients from Cambodia, mainland China and Hong Kong, India, Indonesia, Japan, Malaysia, Papua New Guinea, the Philippines, Singapore, South Korea, Taiwan and Thailand.Findings indicated that less-than-annual site-reported VL testing was associated with poorer treatment outcomes, including a 35% increased risk of acquired immunodeficiency syndrome (AIDS) or death. The prevalence of RAMs in our treatment-naïve patients from any drug class was 13.8%. Predominant HIV-1 genotypes were CRF01_AE and subtype B. Males and patients reporting HIV exposure as homosexual contact had a higher odds of being infected with subtype B. I found that treatment-naïve patients infected with CRF01_AE had lower changes in CD4 counts 12 months post-therapy.Results suggest the need for appropriate monitoring of VL, to improve patient treatment outcomes, and of HIVDR, to inform of risks to standardised regimen efficacy. Genotype tracking of regional variants will help to identify increasing incidence of HIV-1 genotypes in at-risk groups and contribute to monitoring HIV-1 diversity and proliferation in the region