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Jichang Guo - IEEE Xplore Author Profile

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Author details

Jichang Guo

Also published under: Ji-Chang Guo

Publications

26

Citations

2,722

Publications by Year

20082024

Co-Authors:

Guanhua AnCosmin AncutiCodruta O. AncutiAditya AroraChristoph Busch

Show All Co-Authors (140)

Jichang Guo

Also published under: Ji-Chang Guo

Affiliation

School of Electrical and Information Engineering

Tianjin University

Tianjin, China

Shanghai Artificial Intelligence Laboratory, Shanghai, China

Publication Topics

Attention Mechanism,
Object Detection,
Semantic Segmentation,
Underwater Image,
Attention Module,
CNN-based Methods,
Detection Performance,
Feature Maps,
Image Object Detection,
Image Reconstruction,
Loss Function,
Multi-level Features

Biography

Jichang Guo received the B.S. degree from the Nanjing University of Science and Technology, Nanjing, China, in 1988, and the M.S. and Ph.D. degrees in signal and information processing from the School of Electronic Information Engineering, Tianjin University, Tianjin, China, in 1993 and 2003, respectively.,Since 1993, he has been with the School of Electronic Information Engineering, Tianjin University, where he is currently a Full Professor with the School of Electrical and Information Engineering. From 2005 to 2006, he was a Senior Visiting Scholar with Tokyo University of Science, Tokyo, Japan. His current research interests include digital video/image intelligent analysis, recognition and processing, and filter theory and design.(Based on document published on 30 January 2018).

Publications

26

Citations

2,722

Publications by Year

20082024

Co-Authors:

Guanhua An
Cosmin Ancuti
Codruta O. Ancuti
Aditya Arora
Christoph Busch

Show All Co-Authors (140)

Author's Published Works

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Showing 1-25 of 57 results

Conferences (36)

Journals (21)

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Adams Wai-Kin Kong(34)
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Noah Craft(8)
Chaoying Tang(6)
Yanzhu Liu(5)
School of Computer Engineering, Nanyang Technological University, Singapore(16)
School of Computer Science and Engineering, Nanyang Technological University, Singapore(13)
Nanyang Technological University, Singapore, Singapore, SG(8)
Nanyang Technological University, Singapore(5)
Forensics and Security Laboratory, School of Computer Engineering, Nanyang Technological University, Singapore(4)
IEEE Biometrics Compendium(9)
IEEE RFIC Virtual Journal(8)
IEEE RFID Virtual Journal(8)
IEEE Transactions on Information Forensics and Security(6)
IEEE Transactions on Power Systems(4)
IEEE(57)
Media(3)
Hong Kong, China(3)
Orlando, FL, USA(3)
Stockholm, Sweden(3)
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Skin Markers(18)
Gabor Filters(15)
Color Images(14)
Child Pornography(13)
Neural Network(11)

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Flexible-Modal Deception Detection with Audio-Visual Adapter

Zhaoxu Li;Zitong Yu;Xun Lin;Nithish Muthuchamy Selvaraj;Xiaobao Guo;Bingquan Shen;Adams Wai-Kin Kong;Alex Kot

2024 IEEE International Joint Conference on Biometrics (IJCB)

Year: 2024 | Conference Paper |

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Deception detection within audio-visual modalities is vital across diverse sectors, notably in customs security and multimedia anti-fraud. However, this notable efficacy is lost by the necessity to train and deploy separate models for each conceivable modality scenario, leading to redundancy and inefficiency. Moreover, real-world environments where multi-modal models are deployed often fail to mee...Show More

Flexible-Modal Deception Detection with Audio-Visual Adapter

Zhaoxu Li;Zitong Yu;Xun Lin;Nithish Muthuchamy Selvaraj;Xiaobao Guo;Bingquan Shen;Adams Wai-Kin Kong;Alex Kot

2024 IEEE International Joint Conference on Biometrics (IJCB)

Year: 2024 | Conference Paper |

MACE: Mass Concept Erasure in Diffusion Models

Shilin Lu;Zilan Wang;Leyang Li;Yanzhu Liu;Adams Wai-Kin Kong

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (4)

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The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper, we introduce MACE, a finetuning framework for the task of MAss Concept Erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods a...Show More

MACE: Mass Concept Erasure in Diffusion Models

Shilin Lu;Zilan Wang;Leyang Li;Yanzhu Liu;Adams Wai-Kin Kong

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention

Yueming Yin;Hilbert Yuen In Lam;Yuguang Mu;Hoi-Yeung Li;Adams Wai-Kin Kong

IEEE Journal of Biomedical and Health Informatics

Year: 2024 | Volume: 28, Issue: 12 | Journal Article |

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Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate screening by swiftly identifying potential active molecules. The popular deep learning methods in bioactivity prediction primarily model the lig...Show More

Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention

Yueming Yin;Hilbert Yuen In Lam;Yuguang Mu;Hoi-Yeung Li;Adams Wai-Kin Kong

IEEE Journal of Biomedical and Health Informatics

Year: 2024 | Volume: 28, Issue: 12 | Journal Article |

Bridging the Gap Between Vitiligo Segmentation and Clinical Scores

Yanling Li;Steven Tien Guan Thng;Adams Wai-Kin Kong

IEEE Journal of Biomedical and Health Informatics

Year: 2024 | Volume: 28, Issue: 3 | Journal Article |

Cited by: Papers (1)

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Quantitative evaluation of vitiligo is crucial for assessing treatment response. Dermatologists evaluate vitiligo regularly to adjust their treatment plans, which requires extra work. Furthermore, the evaluations may not be objective due to inter- and intra-assessor variability. Though automatic vitiligo segmentation methods provide an objective evaluation, previous methods mainly focus on patch-w...Show More

Bridging the Gap Between Vitiligo Segmentation and Clinical Scores

Yanling Li;Steven Tien Guan Thng;Adams Wai-Kin Kong

IEEE Journal of Biomedical and Health Informatics

Year: 2024 | Volume: 28, Issue: 3 | Journal Article |

TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation

Bingzhi Chen;Yishu Liu;Zheng Zhang;Guangming Lu;Adams Wai Kin Kong

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

Cited by: Papers (81)

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Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry. In recent years, convolutional encoder-decoder solutions have achieved substantial progress in the field of automatic medical image segmentation. Due to the inherent bias in the convolution operations, prior models mainly focus on local visual cues formed by the neigh...Show More

TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation

Bingzhi Chen;Yishu Liu;Zheng Zhang;Guangming Lu;Adams Wai Kin Kong

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

Xiaobao Guo;Nithish Muthuchamy Selvaraj;Zitong Yu;Adams Wai-Kin Kong;Bingquan Shen;Alex Kot

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively. To address this is...Show More

Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

Xiaobao Guo;Nithish Muthuchamy Selvaraj;Zitong Yu;Adams Wai-Kin Kong;Bingquan Shen;Alex Kot

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition

Shilin Lu;Yanzhu Liu;Adams Wai-Kin Kong

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (19)

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Text-driven diffusion models have exhibited impressive generative capabilities, enabling various image editing tasks. In this paper, we propose TF-ICON, a novel Training-Free Image COmpositioN framework that harnesses the power of text-driven diffusion models for cross-domain image-guided composition. This task aims to seamlessly integrate user-provided objects into a specific visual context. Curr...Show More

TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition

Shilin Lu;Yanzhu Liu;Adams Wai-Kin Kong

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

A Practical Upper Bound for the Worst-Case Attribution Deviations

Fan Wang;Adams Wai-Kin Kong

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against t...Show More

A Practical Upper Bound for the Worst-Case Attribution Deviations

Fan Wang;Adams Wai-Kin Kong

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Pace-Adaptive and Noise-Resistant Contrastive Learning for Multimodal Feature Fusion

Xiaobao Guo;Alex Kot;Adams Wai-Kin Kong

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Cited by: Papers (2)

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Multimodal feature fusion aims to draw complementary information from different modalities to achieve better performance. Contrastive learning is effective at discriminating coexisting semantic features (positive) from irrelative ones (negative) in multimodal signals. However, positive and negative pairs learn at separate rates, which undermines the overall performance of multimodal contrastive le...Show More

Pace-Adaptive and Noise-Resistant Contrastive Learning for Multimodal Feature Fusion

Xiaobao Guo;Alex Kot;Adams Wai-Kin Kong

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Real-Time Energy Management for Marine Applications Using Markov Approximation

Thant Zin Oo;Adams Wai-Kin Kong

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 5 | Journal Article |

Cited by: Papers (7)

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All-electric and hybrid-electric ships have become the centerpiece for reducing greenhouse gas emissions and improving fuel efficiency in the maritime industry. Real-time power management of multiple power sources in both design and operation becomes critical due to the uncertainty and randomness of the vessel's power demand. Furthermore, the vessel operators may have additional requirements for o...Show More

Real-Time Energy Management for Marine Applications Using Markov Approximation

Thant Zin Oo;Adams Wai-Kin Kong

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 5 | Journal Article |

Real-Time Shipboard Power Management Based on Monte-Carlo Tree Search

Yan Ren;Adams Wai-Kin Kong;Yi Wang

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 4 | Journal Article |

Cited by: Papers (1)

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The increasing concern in reducing greenhouse gas emissions and improving the fuel efficiency of marine transportation leads to a higher demand for intelligent power management systems (PMS). Unlike offline PMS with prior knowledge on the load profiles, real-time PMS is more challenging because of unknown load profiles. Two typical real-time PMS methods are equivalent consumption minimization stra...Show More

Real-Time Shipboard Power Management Based on Monte-Carlo Tree Search

Yan Ren;Adams Wai-Kin Kong;Yi Wang

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 4 | Journal Article |

An Image Inpainting Approach to Short-Term Load Forecasting

Yanzhu Liu;Shreya Dutta;Adams Wai Kin Kong;Chai Kiat Yeo

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 1 | Journal Article |

Cited by: Papers (26)

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In current power systems, electrical energy is generated whenever there is a demand for it. Therefore, load forecasting, which estimates the active load in advance, is imperative for power system planning and operations. Based on the time horizon, load forecasting is classified as very short-term (below one day), short-term (a day to two weeks), medium-term (two weeks to three years) and long-term...Show More

An Image Inpainting Approach to Short-Term Load Forecasting

Yanzhu Liu;Shreya Dutta;Adams Wai Kin Kong;Chai Kiat Yeo

IEEE Transactions on Power Systems

Year: 2023 | Volume: 38, Issue: 1 | Journal Article |

Deep Multimodal Sequence Fusion by Regularized Expressive Representation Distillation

Xiaobao Guo;Adams Wai-Kin Kong;Alex Kot

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Cited by: Papers (10)

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Multimodal sequence learning aims to utilize information from different modalities to enhance overall performance. Mainstream works often follow an intermediate-fusion pipeline, which explores both modality-specific and modality-supplementary information for fusion. However, the unaligned and heterogeneously distributed multimodal sequences pose significant challenges to the fusion task: 1) to ext...Show More

Deep Multimodal Sequence Fusion by Regularized Expressive Representation Distillation

Xiaobao Guo;Adams Wai-Kin Kong;Alex Kot

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Portmanteauing Features for Scene Text Recognition

Yew Lee Tan;Ernest Yu Kai Chew;Adams Wai-Kin Kong;Jung-Jae Kim;Joo Hwee Lim

2022 26th International Conference on Pattern Recognition (ICPR)

Year: 2022 | Conference Paper |

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Scene text images have different shapes and are subjected to various distortions, e.g. perspective distortions. To handle these challenges, the state-of-the-art methods rely on a rectification network, which is connected to the text recognition network. They form a linear pipeline which uses text rectification on all input images, even for images that can be recognized without it. Undoubtedly, the...Show More

Portmanteauing Features for Scene Text Recognition

Yew Lee Tan;Ernest Yu Kai Chew;Adams Wai-Kin Kong;Jung-Jae Kim;Joo Hwee Lim

2022 26th International Conference on Pattern Recognition (ICPR)

Year: 2022 | Conference Paper |

Power System Design Optimization for a Ferry Using Hybrid-Shaft Generators

Thant Zin Oo;Yan Ren;Adams Wai-Kin Kong;Yi Wang;Xiong Liu

IEEE Transactions on Power Systems

Year: 2022 | Volume: 37, Issue: 4 | Journal Article |

Cited by: Papers (7)

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Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery mus...Show More

Power System Design Optimization for a Ferry Using Hybrid-Shaft Generators

Thant Zin Oo;Yan Ren;Adams Wai-Kin Kong;Yi Wang;Xiong Liu

IEEE Transactions on Power Systems

Year: 2022 | Volume: 37, Issue: 4 | Journal Article |

Segmenting Vitiligo on Clinical Face Images Using CNN Trained on Synthetic and Internet Images

Yanling LI;Adams Wai-Kin Kong;Steven Thng

IEEE Journal of Biomedical and Health Informatics

Year: 2021 | Volume: 25, Issue: 8 | Journal Article |

Cited by: Papers (11)

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Accurately diagnosing and describing the severity of vitiligo is crucial for prognostication, treatment selection and comparison. Currently, disease severity scores require dermatologists to estimate percentage area of involvement, which is subjected to inter and intra-assessor variability. Previous studies focus on pure skin but vitiligo on the face, which has a more serious impact on patients' q...Show More

Segmenting Vitiligo on Clinical Face Images Using CNN Trained on Synthetic and Internet Images

Yanling LI;Adams Wai-Kin Kong;Steven Thng

IEEE Journal of Biomedical and Health Informatics

Year: 2021 | Volume: 25, Issue: 8 | Journal Article |

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Wojciech Michal Matkowski;Tingting Chai;Adams Wai Kin Kong

IEEE Transactions on Information Forensics and Security

Year: 2020 | Volume: 15 | Journal Article |

Cited by: Papers (91)

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Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these tw...Show More

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Wojciech Michal Matkowski;Tingting Chai;Adams Wai Kin Kong

IEEE Transactions on Information Forensics and Security

Year: 2020 | Volume: 15 | Journal Article |

Palmprint Recognition Using Realistic Animation Aided Data Augmentation

Swarup Pranjal;Adams Wai Kin Kong

2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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In this paper, a palmprint augmentation algorithm based on 3D animation is proposed for enhancing contactless palmprint recognition performance. Contactless palmprint varies in position, orientation and musculoskeletal deformations. As the existing contactless databases are small, they contain only a few such variations of a palm. Popular data augmentation approaches, including translation, rotati...Show More

Palmprint Recognition Using Realistic Animation Aided Data Augmentation

Swarup Pranjal;Adams Wai Kin Kong

2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS)

Year: 2019 | Conference Paper |

The Nipple-Areola Complex for Criminal Identification

Wojciech Michal Matkowski;Krzysztof Matkowski;Adams Wai-Kin Kong;Cory Lloyd Hall

2019 International Conference on Biometrics (ICB)

Year: 2019 | Conference Paper |

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In digital and multimedia forensics, identification of child sexual offenders based on digital evidence images is highly challenging due to the fact that the offender's face or other obvious characteristics such as tattoos are occluded, covered, or not visible at all. Nevertheless, other naked body parts, e.g., chest are still visible. Some researchers proposed skin marks, skin texture, vein or an...Show More

The Nipple-Areola Complex for Criminal Identification

Wojciech Michal Matkowski;Krzysztof Matkowski;Adams Wai-Kin Kong;Cory Lloyd Hall

2019 International Conference on Biometrics (ICB)

Year: 2019 | Conference Paper |

Giant Panda Face Recognition Using Small Dataset

Wojciech Michal Matkowski;Adams Wai Kin Kong;Han Su;Peng Chen;Rong Hou;Zhihe Zhang

2019 IEEE International Conference on Image Processing (ICIP)

Year: 2019 | Conference Paper |

Cited by: Papers (14)

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Giant panda (panda) is a highly endangered animal. Significant efforts and resources have been put on panda conservation. To measure effectiveness of conservation schemes, estimating its population size in wild is an important task. The current population estimation approaches, including capture-recapture, human visual identification and collection of DNA from hair or feces, are invasive, subjecti...Show More

Giant Panda Face Recognition Using Small Dataset

Wojciech Michal Matkowski;Adams Wai Kin Kong;Han Su;Peng Chen;Rong Hou;Zhihe Zhang

2019 IEEE International Conference on Image Processing (ICIP)

Year: 2019 | Conference Paper |

A Constrained Deep Neural Network for Ordinal Regression

Chi Keong Goh;Yanzhu Liu;Adams Wai Kin Kong

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (14)

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Ordinal regression is a supervised learning problem aiming to classify instances into ordinal categories. It is challenging to automatically extract high-level features for representing intraclass information and interclass ordinal relationship simultaneously. This paper proposes a constrained optimization formulation for the ordinal regression problem which minimizes the negative loglikelihood fo...Show More

A Constrained Deep Neural Network for Ordinal Regression

Chi Keong Goh;Yanzhu Liu;Adams Wai Kin Kong

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

A Meta-Recognition Based Skin Marks Matching Algorithm with Feature Fusion for Forensic Identification

Peicong Yu;Adams Wai-Kin Kong

2018 International Conference on Biometrics (ICB)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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Soft biometrics, such as skin marks, play an important role in forensic identification, for they cannot only supplement hard biometrics to improve the overall identification performance, but may also serve as supportive evidence when hard biometrics is not available. Skin marks are small and difficult to be accurately detected due to different lighting conditions, poses as well as individual varia...Show More

A Meta-Recognition Based Skin Marks Matching Algorithm with Feature Fusion for Forensic Identification

Peicong Yu;Adams Wai-Kin Kong

2018 International Conference on Biometrics (ICB)

Year: 2018 | Conference Paper |

A Study of Distinctiveness of Skin Texture for Forensic Applications Through Comparison With Blood Vessels

Frodo Kin Sun Chan;Xiaojie Li;Adams Wai-Kin Kong

IEEE Transactions on Information Forensics and Security

Year: 2017 | Volume: 12, Issue: 8 | Journal Article |

Cited by: Papers (11) | Patents (1)

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Skin texture without obvious features is different from other hard biometrics on the skin, such as fingerprints and palmprints. Skin texture gives an impression that it is not distinctive like other soft biometric traits. It was proposed for personal identification a decade ago but did not draw attention from the biometric community, partially due to the success of other biometric technologies for...Show More

A Study of Distinctiveness of Skin Texture for Forensic Applications Through Comparison With Blood Vessels

Frodo Kin Sun Chan;Xiaojie Li;Adams Wai-Kin Kong

IEEE Transactions on Information Forensics and Security

Year: 2017 | Volume: 12, Issue: 8 | Journal Article |

A geometric-based tattoo retrieval system

Xingpeng Xu;Adams Wai Kin Kong

2016 23rd International Conference on Pattern Recognition (ICPR)

Year: 2016 | Conference Paper |

Cited by: Papers (3)

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Various soft biometric traits have been used as hints in forensic investigation. Tattoo, as one of those soft biometric traits, has been used extensively because it is easy to be remembered and described by witnesses and appears very often among criminals and victims. Most of the tattoo retrieval systems currently used in police departments are still text-based systems. They depend on labels tagge...Show More

A geometric-based tattoo retrieval system

Xingpeng Xu;Adams Wai Kin Kong

2016 23rd International Conference on Pattern Recognition (ICPR)

Year: 2016 | Conference Paper |

Tattoo detection based on CNN and remarks on the NIST database

Qingyong Xu;Soham Ghosh;Xingpeng Xu;Yi Huang;Adams Wai Kin Kong

2016 International Conference on Biometrics (ICB)

Year: 2016 | Conference Paper |

Cited by: Papers (3)

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Detecting tattoo images stored in information technology (IT) devices of suspects is an important but challenging task for law enforcement agencies. Recently, the U.S. National Institute of Standards and Technology (NIST) held a challenge and released a tattoo database for the commercial and academic community in advancing research and development into automated image-based tattoo recognition tech...Show More

Tattoo detection based on CNN and remarks on the NIST database

Qingyong Xu;Soham Ghosh;Xingpeng Xu;Yi Huang;Adams Wai Kin Kong

2016 International Conference on Biometrics (ICB)

Year: 2016 | Conference Paper |

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