Fingerprint Recognition System by Termination Points Using Cascade-Forward Backpropagation Neural Network

Agarwal, Annu; Sharma, Ajay; Khandelwal, Sarika

doi:10.1007/978-981-10-0755-2_22

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…The Gabor filter is the real part of the Gabor function, which is composed by a cosine wave modulated by a Gaussian. The general formula of the symmetric Gabor filter comes by (12). (12) x θ = x cosθ + y sinθ (13)…”

Section: Gabor Filtermentioning

confidence: 99%

“…Werner extracts fifteen minutiae of the area of interest which is located in the center of the fingerprint and with this, trains the neuronal networks, using the architecture of the multilayer perceptron [10]- [11]. Likewise, algorithms that use fingerprint minutia have been developed, but in this case it only takes the terminations for authentication, obtaining more than 90% of accuracy when a fingerprint matches the database [12]. Therefore, in this paper a biometric digital system is developed based on fingerprints that aims to provide greater ease and safety when accessing a site, based on neural networks so that if the fingerprint is physically modified, there is no impediment for the user to be identified.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprints detection using neural networks suitable to physical changes of fingerprint

2017

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This working paper shows the results of finished research, using image processing techniques to improve the fingerprint obtained from a database, where the image is normalized and segmented to get only the section of the image with the fingerprint. Then, the Gabor filter is applied, and it corrects defects in ridges and valleys, allowing continuity. That way, if the fingerprint has a physical defect, the filter can correct it as long as the segment orientation to be correct. Once improved, the fingerprint, it is binarized and thinned for minutiae extraction. The false minutiae are filtered and eliminated in order to ensure the operation of the algorithm. Finally, it is necessary training with the minutiae of all fingerprints in the database, to individually determine which user belongs the fingerprint entered. The system has a reliability of 81% of the process, with the pre-processing part being crucial to guarantee the correct extraction of the characteristics of fingerprints.

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Section: Gabor Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fingerprints detection using neural networks suitable to physical changes of fingerprint

2017

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“…In an era characterized by burgeoning population densities and escalating concerns over the deleterious consequences of emissions from fossil fuel-dependent systems on both human well-being and the ecological landscape, the imperative for the development and adoption of efficacious clean energy solutions is more pressing than ever [1][2][3]. A prom-inent contender within the clean energy pantheon is the solar photovoltaic (PV) system.…”

Section: Introductionmentioning

confidence: 99%

Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks

Alghamdi

Maduabuchi

Okoli

et al. 2023

International Journal of Energy Research

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In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions.

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Analysis of Shape Signature in First and Second Derivatives by Using Wavelet Transformation

Mani¹,

Jagadesh²,

Satyanarayana

et al. 2020

Lecture Notes in Electrical Engineering

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Fingerprint Recognition System by Termination Points Using Cascade-Forward Backpropagation Neural Network

Cited by 6 publications

References 4 publications

Fingerprints detection using neural networks suitable to physical changes of fingerprint

Fingerprints detection using neural networks suitable to physical changes of fingerprint

Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks

Analysis of Shape Signature in First and Second Derivatives by Using Wavelet Transformation

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