A Spoofing Security Approach for Facial Biometric Data Authentication in Unconstraint Environment

Kumar, Naresh; Sharma, Aditi

doi:10.1007/978-981-13-1708-8_40

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…e receptive field of a neuron is the extent of its scope in input data, and it is increased by stacking the convolution layers. e convolution operation is given as equation (1), where CB k is the k th convolution kernel weights and its bias term, respectively, and is expressing the convolution method.…”

Section: Convolution Neural Layermentioning

confidence: 99%

“…The biometric system utilizes an individual's physiological or behavioural characteristics for identification, verification, and authentication. The invariable physiological characteristics include DNA, iris, fingerprint, palm, and facial expression [ 1 , 2 ], whereas behavioural traits cover voice, signature, and handwriting [ 1 , 3 , 4 ]. Physical characteristics such as fingerprint and iris are often used because of their high performance.…”

Section: Introductionmentioning

confidence: 99%

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Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Kumar

Saraswat

Ather

et al. 2022

Computational Intelligence and Neuroscience

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Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature’s scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.

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Section: Convolution Neural Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Kumar

Saraswat

Ather

et al. 2022

Computational Intelligence and Neuroscience

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“…Similar to most Google services and features, the entire history and management are fully connected to the Google user account similar to most features and services offered by Google. GLH has been used to evaluate the habits of the users, as explained in Kumar and Sharma ( 2019 ), and the mobility of the user, as presented in Cheng et al ( 2020 ). Besides, GLH helps in visualization, as studied by Zhou et al ( 2020 ), including infrastructure planning, infectious disease control, and appropriate response to disastrous occasions.…”

Section: Experimental Setup and Working Strategy Of Proposed Smart Maskmentioning

confidence: 99%

IoT-Based Smart Mask Protection against the Waves of COVID-19

Goar

Sharma

Yadav

et al. 2022

J Ambient Intell Human Comput

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In the year 2020, the word “pandemic” has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a “Pandemic”. The outbreak pattern differs widely across the globe based on the findings discovered so far; however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images; the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology; therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytics. The manuscript and associated research work integrate the IoT and Internet of Everything (IoE) based analytics with sensor technologies with real-time data so that the overall predictions will be more accurate and integrated with the health sector. Supplementary Information The online version contains supplementary material available at 10.1007/s12652-022-04395-7.

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Utilizing high computational ML model for lung cancer prediction with accurate data analysis

Paul,

Ibrahim,

Sharma

2023

Machine Learning and Information Processing: Proceedings of Icmlip 2023

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A Spoofing Security Approach for Facial Biometric Data Authentication in Unconstraint Environment

Cited by 9 publications

References 22 publications

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

IoT-Based Smart Mask Protection against the Waves of COVID-19

Utilizing high computational ML model for lung cancer prediction with accurate data analysis

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