Multispectral face spoofing detection using VIS–NIR imaging correlation

2018 ◽  
Vol 16 (02) ◽  
pp. 1840003 ◽  
Author(s):  
Xudong Sun ◽  
Lei Huang ◽  
Changping Liu
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Experimental Results ◽  
Security Systems ◽  
Local Descriptors ◽  
Confidence Factor ◽  
Nir Imaging ◽  
Spoofing Detection ◽  
Factor Map ◽  
Face Spoofing

With the wide applications of face recognition techniques, spoofing detection is playing an important role in the security systems and has drawn much attention. This research presents a multispectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging, which exploits VIS–NIR image consistency for spoofing detection. First, we use part-based methods to extract illumination robust local descriptors, and then the consistency is calculated to perform spoofing detection. In order to further exploit multispectral correlation in local patches and to be free from manually chosen regions, we learn a confidence factor map for all the patches, which is used in final classifier. Experimental results of self-collected datasets, public Msspoof and PolyU-HSFD datasets show that the proposed approach gains promising results for both intra-dataset and cross-dataset testing scenarios, and that our method can deal with different illumination and both photo and screen spoofing.

scholarly journals Visible/Infrared face spoofing detection using texture descriptors

2019 ◽  
Vol 292 ◽  
pp. 04006
Author(s):  
Shaimaa Mohamed ◽  
Amr Ghoneim ◽  
Aliaa Youssif
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Spectral Imaging ◽  
Experimental Results ◽  
Visible Range ◽  
Security Systems ◽  
Spoofing Detection ◽  
Public Datasets ◽  
Face Spoofing

With extensive applications of face recognition technologies, face anti-spoofing played an important role and has drawn a great attention in the security systems. This study represents a multi-spectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging. Spectral imaging is the capture of images in multiple bands. Since these attacks are carried out at the sensor, operating in the visible range, a sensor operating in another band can give more cues regarding the artifact or disguise used to carry out the attack. Our experimental results of public datasets proved that the proposed algorithms gain promising results for different testing scenarios and that our methods can deal with different illuminations and both photo and screen spoofing.

scholarly journals Deep Discriminative Restricted Boltzmann Machine (DDRBM) for Robust Face Spoofing Detection

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Gustavo Botelho de Souza ◽  
Joao Paulo Papa ◽  
Aparecido Nilceu Marana
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Attack Detection ◽  
Restricted Boltzmann Machine ◽  
Digital Cameras ◽  
Boltzmann Machine ◽  
Robust Solution ◽  
Spoofing Detection ◽  
Recognition Systems ◽  
Face Spoofing

Biometrics emerged as a robust solution for security systems. Despite, nowadays criminals are developing techniques to accurately simulate biometric traits of valid users, process known as spoofing attack, in order to circumvent the biometric applications. Face is among the main biometric characteristics, being extremely convenient for users given its non-intrusive capture by means of digital cameras. However, face recognition systems are the ones that most suffer with spoofing attacks since such cameras, in general, can be easily fooled with common printed photographs. In this sense, countermeasure techniques should be developed and integrated to the traditional face recognition systems in order to prevent such frauds. Among the main neural networks for face spoofing detection is the discriminative Restricted Boltzmann Machine (RBM) which, besides of efficiency, achieves great results in attack detection by learning the distributions of real and fake facial images. However, it is known that deeper neural networks present better accuracy results in many tasks. In this context, we propose a novel model called Deep Discriminative Restricted Boltzmann Machine (DDRBM) applied to face spoofing detection. Results on the NUAA dataset show a significative improvement in performance when compared to the accuracy rates of a traditional discriminative RBM on attack detection.

The Role of NIR Fluorescence in MDR Cancer Treatment: From Targeted Imaging to Phototherapy

2020 ◽  
Vol 27 (33) ◽  
pp. 5510-5529
Author(s):  
Zengtao Wang ◽  
Qingqing Meng ◽  
Shaoshun Li
Keyword(s):  
Cancer Treatment ◽  
Near Infrared ◽  
Combined Therapy ◽  
Efflux Pumps ◽  
Cross Resistance ◽  
Great Promise ◽  
Drug Efflux ◽  
Nir Dyes ◽  
Nir Imaging ◽  
Drug Efflux Pumps

Background: Multidrug Resistance (MDR) is defined as a cross-resistance of cancer cells to various chemotherapeutics and has been demonstrated to correlate with drug efflux pumps. Visualization of drug efflux pumps is useful to pre-select patients who may be insensitive to chemotherapy, thus preventing patients from unnecessary treatment. Near-Infrared (NIR) imaging is an attractive approach to monitoring MDR due to its low tissue autofluorescence and deep tissue penetration. Molecular NIR imaging of MDR cancers requires stable probes targeting biomarkers with high specificity and affinity. Objective: This article aims to provide a concise review of novel NIR probes and their applications in MDR cancer treatment. Results: Recently, extensive research has been performed to develop novel NIR probes and several strategies display great promise. These strategies include chemical conjugation between NIR dyes and ligands targeting MDR-associated biomarkers, native NIR dyes with inherent targeting ability, activatable NIR probes as well as NIR dyes loaded nanoparticles. Moreover, NIR probes have been widely employed for photothermal and photodynamic therapy in cancer treatment, which combine with other modalities to overcome MDR. With the rapid advancing of nanotechnology, various nanoparticles are incorporated with NIR dyes to provide multifunctional platforms for controlled drug delivery and combined therapy to combat MDR. The construction of these probes for MDR cancers targeted NIR imaging and phototherapy will be discussed. Multimodal nanoscale platform which integrates MDR monitoring and combined therapy will also be encompassed. Conclusion: We believe these NIR probes project a promising approach for diagnosis and therapy of MDR cancers, thus holding great potential to reach clinical settings in cancer treatment.

scholarly journals Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1548 ◽  
Author(s):  
Kyuseok Kim ◽  
Hyun-Woo Jeong ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Near Infrared ◽  
Imaging System ◽  
Histogram Equalization ◽  
Evaluation Performance ◽  
Visual Evaluation ◽  
Évaluation Performance ◽  
Nir Imaging ◽  
Relative Total Variation

Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.

Development of a Novel SNAP-Epitope Tag/Near-Infrared Imaging Assay to Quantify G Protein-Coupled Receptor Degradation in Human Cells

2021 ◽  
pp. 247255522097979
Author(s):  
Kyung-Soon Lee ◽  
Edelmar Navaluna ◽  
Nicole M. Marsh ◽  
Eric M. Janezic ◽  
Chris Hague
Keyword(s):  
G Protein ◽  
Adrenergic Receptor ◽  
Near Infrared ◽  
Human Cells ◽  
Receptor Subtypes ◽  
G Protein Coupled Receptor ◽  
Functional Responses ◽  
Epitope Tag ◽  
Nir Imaging ◽  
G Protein Coupled

We have developed a novel reporter assay that leverages SNAP-epitope tag/near-infrared (NIR) imaging technology to monitor G protein-coupled receptor (GPCR) degradation in human cell lines. N-terminal SNAP-tagged GPCRs were subcloned and expressed in human embryonic kidney (HEK) 293 cells and then subjected to 24 h of cycloheximide (CHX)-chase degradation assays to quantify receptor degradation half-lives ( t1/2) using LICOR NIR imaging–polyacrylamide gel electrophoresis (PAGE) analysis. Thus far, we have used this method to quantify t1/2 for all nine adrenergic (ADRA1A, ADRA1B, ADRA1D, ADRA2A, ADRA2B, ADRA2C, ADRB1, ADRB2, ADRB3), five somatostatin (SSTR1, SSTR2, SSTR3, SSTR4, SSTR5), four chemokine (CXCR1, CXCR2, CXCR3, CXCR5), and three 5-HT2 (5HT2A, 5HT2B, 5HT2C) receptor subtypes. SNAP-GPCR-CHX degradation t1/2 values ranged from 0.52 h (ADRA1D) to 5.5 h (SSTR3). On the contrary, both the SNAP-tag alone and SNAP-tagged and endogenous β-actin were resistant to degradation with CHX treatment. Treatment with the proteasome inhibitor bortezomib produced significant but variable increases in SNAP-GPCR protein expression levels, indicating that SNAP-GPCR degradation primarily occurs through the proteasome. Remarkably, endogenous β2-adrenergic receptor/ADRB2 dynamic mass redistribution functional responses to norepinephrine were significantly decreased following CHX treatment, with a time course equivalent to that observed with the SNAP-ADRB2 degradation assay. We subsequently adapted this assay into a 96-well glass-bottom plate format to facilitate high-throughput GPCR degradation screening. t1/2 values quantified for the α1-adrenergic receptor subtypes (ADRA1A, ADRA1B, ADR1D) using the 96-well-plate format correlated with t1/2 values quantified using NIR-PAGE imaging analysis. In summary, this novel assay permits precise quantitative analysis of GPCR degradation in human cells and can be readily adapted to quantify degradation for any membrane protein of interest.

A Comparison of Near-Infrared Imaging and Computerized Tomography Scan for Detecting Maxillary Sinusitis

2021 ◽  
pp. 000348942110606
Author(s):  
Mehdi Abouzari ◽  
Brooke Sarna ◽  
Joon You ◽  
Adwight Risbud ◽  
Kotaro Tsutsumi ◽  
...  
Keyword(s):  
Maxillary Sinus ◽  
Computerized Tomography ◽  
Sensitivity And Specificity ◽  
Near Infrared ◽  
Maxillary Sinusitis ◽  
Tertiary Care ◽  
Pixel Intensity ◽  
Sinus Disease ◽  
Nir Imaging ◽  
Average Pixel Intensity

Objective: To investigate the use of near-infrared (NIR) imaging as a tool for outpatient clinicians to quickly and accurately assess for maxillary sinusitis and to characterize its accuracy compared to computerized tomography (CT) scan. Methods: In a prospective investigational study, NIR and CT images from 65 patients who presented to a tertiary care rhinology clinic were compared to determine the sensitivity and specificity of NIR as an imaging modality. Results: The sensitivity and specificity of NIR imaging in distinguishing normal versus maxillary sinus disease was found to be 90% and 84%, normal versus mild maxillary sinus disease to be 76% and 91%, and mild versus severe maxillary sinus disease to be 96% and 81%, respectively. The average pixel intensity was also calculated and compared to the modified Lund-Mackay scores from CT scans to assess the ability of NIR imaging to stratify the severity of maxillary sinus disease. Average pixel intensity over a region of interest was significantly different ( P < .001) between normal, mild, and severe disease, as well as when comparing normal versus mild ( P < .001, 95% CI 42.22-105.39), normal versus severe ( P < .001, 95% CI 119.43-174.14), and mild versus severe ( P < .001, 95% CI 41.39-104.56) maxillary sinus disease. Conclusion: Based on this data, NIR shows promise as a tool for identifying patients with potential maxillary sinus disease as well as providing information on severity of disease that may guide administration of appropriate treatments.

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