Automated Biometric Personal Identification-Techniques and Applications

Rai, Vinayak; Mehta, Kapil; Jatin, Jatin; Tiwari, Dheeraj; Chaurasia, Rohit

doi:10.1109/iciccs48265.2020.9120896

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…In this manner installing crop development models (CGMs) into the Internet of Things application framework would make the agribusiness framework increasingly astute and versatile. We can accumulate constant information dependent on conditions that exist, utilizing remote innovation, for example, sensors, so Agriculturalists can take activities quickly (15). They can settle on very much established choices for their yields dependent on climate conditions, dampness and stickiness, compound pieces of the dirt.…”

Section: Related Workmentioning

confidence: 99%

“…Internet of things can be depicted upon for Information and Communication Technology as an extravagant term for a situation where anything might be embedded in a system, be particularly recognized, and interface with insignificant human intercession. These things can have a place with this present reality both from lifeless pieces and to living creatures, or the virtual world (virtual "things") that solitary exists in a re-enactment domain (2). To explain, a thing is a customary gadget that can be extraordinarily recognized and associated with the Internet.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Smart Agriculture Using Internet of Things

Mehta

Arora

Arora³

et al. 2022

ECS Trans.

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Internet of Things (IoT) is an innovation that reprimands the fate of correspondence and registering. In present times, IoT services are availed in multiple domains like smart homes, shrewd traffic control, urban planning, health infrastructure, and so forth. The utility domain of Internet of Things is quite vast and can be actualized in diverse fields. This paper work is about the execution of Internet of Things in agriculture. The latest advancements of farming with utilization of Internet of Things in agribusiness strategies can be cost effective for agriculturalists. It can help in decreasing the yield wastages and bumper crop yields and help in curbing the issues of hunger and malnutrition. Smart cultivating depending upon Internet of Things advances would help the cultivators and agriculturalists in diminishing the waste and in enhancing the profitability going from the amount of manure used within required technical quality norms. The point is to innovate with a device which creates messages on various stages to advice agriculturalists and help government in their ambitious target of doubling the agriculturalists income by 2022 of 2015 base levels. The item will help agriculturalists by getting live information from the farmland to find a way to empower them to do shrewd cultivating by likewise expanding their harvest yields and sustainable use of inputs. The proposal is a basic engineering of Internet of Things sensors that gather data and send it over the Wi-Fi system to the server. The server can take activities relying upon the data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of Smart Agriculture Using Internet of Things

Mehta

Arora

Arora³

et al. 2022

ECS Trans.

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show abstract

“…Where, c1 and c2 are included to avoid instability when µ 𝑥 2 + µ 𝑦 2 𝑖𝑠 𝑣𝑒𝑟𝑦 𝑠𝑚𝑎𝑙𝑙 𝑎𝑛𝑑 𝑐𝑙𝑜𝑠𝑒 𝑡𝑜 𝑧𝑒𝑟𝑜. c1 and c2 are given as: 𝑐 1 = (𝐾 1 𝐿) 2 where 𝐾 1 = 0.01; 𝑐 2 = (𝐾 2 𝐿) 2 𝑤ℎ𝑒𝑟𝑒 𝐾 2 = 0.03; L = dynamic range of the pixel values.…”

Section: 𝑆𝑆𝐼𝑀(𝑥 𝑦) =mentioning

confidence: 99%

“…Among all the biometrics, fingerprints are the most reliable and useful biometrics system [1]. In the paper, [2] authors commented that fingerprint-based biometric system is less expensive as compared to face and iris-based systems. In the past, many researchers commented on applications of fingerprints and their usability but it laid down in its objective due to presentation attacks [3].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

Verma¹,

Gupta²,

Akbar³

et al. 2021

Preprint

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The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

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“…Various biometric features can be used for this purpose, with each offering unique information for identification. The most common biometric features employed for recognition include fingerprint scanning, iris recognition, and face recognition [ 1 ]. However, all these biometric features require close contact with the subject, which is impossible in some scenarios.…”

Section: Introductionmentioning

confidence: 99%

An end-to-end gait recognition system for covariate conditions using custom kernel CNN

Ali,

Bukhari,

Maqsood

et al. 2024

Heliyon

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Automated Biometric Personal Identification-Techniques and Applications

Cited by 14 publications

References 11 publications

Enhancement of Smart Agriculture Using Internet of Things

Enhancement of Smart Agriculture Using Internet of Things

Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

An end-to-end gait recognition system for covariate conditions using custom kernel CNN

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