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

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

doi:10.1109/tifs.2017.2692684

Cited by 13 publications

(10 citation statements)

References 34 publications

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“…is process is repeated until all the views are rebuilt. After 6 iterations and 10 iterations, the processed reconstruction result can be obtained [23]. e point cloud results are continuously dense and the model reduction is gradually increasing.…”

Section: Visualization Design Of 3d Medical Images Of Ocularmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

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Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

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Section: Visualization Design Of 3d Medical Images Of Ocularmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

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“…As with the protocol in IARPA Janus Benchmark-A [50], which was designed to study unconstrained face recognition for a particular group of subjects, e.g., identification of terrorists or gangsters in a watch list, this protocol allows performing training on the gallery set. Cumulative match characteristic (CMC) curve is used as an evaluation metric, which is common for forensic applications [4], [8], [50], [24]. In addition, rank-1 and rank-30 identification accuracies are also used as evaluation metrics.…”

Section: A Evaluation Protocolsmentioning

confidence: 99%

“…Veins and skin marks require high-resolution image [9]. Some of the body parts have no or not enough skin marks or hair and skin texture may not be discriminative enough if only small skin region is available [8]. Still, open hands can be observed in some of the images [10], [11], particularly when the subjects salute, wave, raise their hands, touch the victim or the offender, or try to cover the camera.…”

Section: Introductionmentioning

confidence: 99%

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Matkowski

Chai

Kong

2020

IEEE Trans.Inform.Forensic Secur.

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104

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Abstract-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 highresolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is endto-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.

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“…The LBP histograms and the Gabor orientation histograms in each block of the dynamic grid system and the directional grid systems are extracted and normalized. The grid systems and the feature descriptors employed in this paper are not specific for androgenic hair and can also be applied to other texture [12].…”

Section: Two Grid Systemsmentioning

confidence: 99%

“…High concentrations of fat and melanin weaken visible light passing through the skin and make blood vessel visualization difficult. Recently, skin texture [12] is also suggested for criminal and victim investigation on low resolution images. However, skin can be covered by dense androgenic hair.…”

Section: Introductionmentioning

confidence: 99%

A further study of low resolution androgenic hair patterns as a soft biometric trait

Chan

Kong

2018

Image and Vision Computing

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

Cited by 13 publications

References 34 publications

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Palmprint Recognition in Uncontrolled and Uncooperative Environment

A further study of low resolution androgenic hair patterns as a soft biometric trait

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