Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Marák, Pavol; Hambalík, Alexander

doi:10.1515/tmmp-2016-0035

Cited by 18 publications

(7 citation statements)

References 16 publications

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“…In order to produce accurate fingerprint matching / recognitions, the CNN based approach is designed and integrated with Atrous Spatial Pyramid Pooling (ASPP) with the minutiae fingerprint feature. For fingerprint matching, a well-known and widely used matching algorithm, NIST BOZORTH3 is used and it establishes a matching score between fingerprints [ 28 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional neural network based children recognition system using contactless fingerprints

Rajaram¹,

Amma

Selvakumar

2023

Int. j. inf. tecnol.

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Biometric features are useful for unique identification, authentication, and security applications. Among all biometric features, fingerprints are the most commonly used because they contain ridges and valleys. There are challenges in recognizing child or infant fingerprints since the ridges are not mature as the hands are covered with a white substance and acquisition of fingerprint images is difficult. In the context of COVID-19 pandemic, contactless fingerprint acquisition gains importance as it is not infectious especially with children. In this study, a Convolutional Neural Network (CNN) based children recognition system named Child-CLEF, that uses Contact-Less Children Fingerprint (CLCF) dataset acquired using a mobile phone-based scanner is proposed. The quality of captured fingerprint images is enhanced using a hybrid image enhancement method. Furthermore, the minutiae features are extracted using the proposed Child-CLEF Net model and the identification of children is made using a matching algorithm. The proposed system is tested with a self-captured children fingerprint dataset, CLCF and publicly available PolyU fingerprint dataset. It is found that the proposed system outperforms the existing fingerprint recognition systems in terms of accuracy and equal error rate.

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Section: Literature Reviewmentioning

confidence: 99%

Convolutional neural network based children recognition system using contactless fingerprints

Rajaram¹,

Amma

Selvakumar

2023

Int. j. inf. tecnol.

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“…The study of [10] explained that the performance of a modern automated fingerprint recognition system is heavily influenced by the accuracy of their fingerprint feature extraction algorithm. Their study investigated the possibilities of integrating artificial neural networks into fingerprint recognition process.…”

Section: Review Of Related Workmentioning

confidence: 99%

Fingerprintbased Attendance Management System

Yahaya¹

2020

ijarcs

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This research was conducted with the broad objective of providing solution to the traditional student attendance system to a fullycomputerized and automated system. The system was designed to improve the existing method of attendance taking by introducing a biometric feature i.e., fingerprint capturing. The system was built using C# and SecuGen fingerprint reader. It has successfully been able to capture the fingerprint image of any individual within a time range of 0.2 to 0.5 seconds, differentiating each captured fingerprint from others which has been captured previously, and recording the input data, there by achieving the objectives of thestudy.Thissystemhasbeenabletocircumventtheproblemofdrudgeryandinefficiency of the conventional method of attendance taking, and it is therefore recommended that this application of biometric system be adopted and used in schools and organizational bodies to reduce impersonation that is common in the old system of verifying candidates.

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“…Efficient and accurate results obtained for fingerprint detection and face recognition using artificial neural net [2,8,11,12,15]. Palm print and biometric hands detection employing back-propagation neural net with Levenberg-Marquardt training algorithm used in the classification of Palm print Biometrics.…”

Section: Related Workmentioning

confidence: 99%

Odour Based Human Identification and Classification using Neural Networks

2019

IJRTE

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Biometrics permits an individual to be authenticated and identified by computer systems following on a set of verifiable and identifiable data that are precise and unique in nature. This mechanism constitutes a cutting-edge method of identifying an individual since it precisely establishes more explicit and direct connection with humans than mere passwords since biometrics tend to use measurable behavioral and physiological characteristics of human. In this paper, a framework for human identification is proposed distinctively based on specific human odour features. 15 samples of female and male human odour are collected from different age groups, only 15 effective Volatile Organic Compounds (VOCs) are chosen. In this paper, several diverse functions of neural network activation are tested such as Levenberg-Marquardt backpropagation, Gradient descent backpropagation, and Resilient backpropagation. Besides, numerous neural network topologies are tested by means of variety hidden layers and different number of neurons and. Different energy functions were tested TAN- Sigmoid transfer, Linear transfer, and LOG- Sigmoid transfer. Considering the obtained results, employing two hidden layers with more neurons in the hidden layers- to be specific: 15 neurons in every layer- has yielded better accuracy in performance with an accuracy rate of 100%. The unsurpassed framework for algorithm learning to be used for human identification can be backpropagation learning algorithm named the Levenberg-Marquardt. The best function for activation established in this paper is the function of TANSigmoid transfer. The performance accuracy consistency in recognizing human can be enhanced using a big number of study samples.

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Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Cited by 18 publications

References 16 publications

Convolutional neural network based children recognition system using contactless fingerprints

Convolutional neural network based children recognition system using contactless fingerprints

Fingerprintbased Attendance Management System

Odour Based Human Identification and Classification using Neural Networks

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