Adaptive fuzzy genetic algorithm for multi biometric authentication

Malarvizhi, N.; Selvarani, P.; Raj, Pethuru

doi:10.1007/s11042-019-7436-4

Cited by 34 publications

(21 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Singh and Kant (2019) have designed a multimodal biometric system based on FKP and iris traits for person authentication, where the PCA method has been used for feature extraction with the Neuro fuzzy neural network (NFNN) classifier in identification step. Malarvizhi et al (2019) proposed a system named adaptive fuzzy genetic algorithm (AFGA) for Biometric authentication. Wang et al (2014) investigated depth neural network for finger print classification.…”

Section: Introductionmentioning

confidence: 99%

Deep rule-based classifier for finger knuckle pattern recognition system

et al. 2020

View full text Add to dashboard Cite

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep rule-based classifier for finger knuckle pattern recognition system

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Shelton et al [19] suggested a procedure to localise the iris that uses a combination of FCM clustering and level set method. Malarvizhi et al [20] have analysed the impact of soft computing strategies in biometrics. Optimisation based on hybrid soft computing called 'adaptive fuzzy genetic algorithm is used for both unimodel and multi-model biometric authentication systems by combining the iris and fingerprint biometrics.…”

Section: Introductionmentioning

confidence: 99%

“…Then, Gaussian mixture model and boundary point selection algorithms are employed to detect pupil and limbus boundaries, respectively. Some other algorithms that have been proposed in the literature for segmentation of iris images captured under non-cooperative conditions [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Efficient method for segmentation of noisy and non‐circular iris images using improved particle swarm optimisation‐based MRFCM

Rapaka¹,

Kumar

2020

IET biom.

View full text Add to dashboard Cite

Segmentation of the iris is a crucial stage in an automated iris-based recognition system. The performance of any biometric system primarily relies on how effectively the iris is extracted from the unwanted parts of an iris image. The process of iris segmentation is mainly affected by the noise artefacts such as eyelid/eyelashes occlusions, specular reflections, intensity inhomogeneities, and non-circularity of the iris boundary. A novel and an efficient method has been proposed in this work to segment noisy and non-circular iris boundaries. The mathematical modelling of morphological reconstruct fuzzy C-means clustering (MRFCM) has been presented. The MRFCM based on improved particle swarm optimisation has been implemented before the segmentation in the recognition framework. The resultant images are then segmented by employing geodesic active contours incorporated by a new stopping function. The effect of the proposed segmentation method on iris recognition is observed through matching score distribution. The popular and publicly available datasets such as UBIRISv1, CASIA-v3-Interval, MMU1, and Mobile Iris Challenge Evaluation databases are considered for the evaluation of the proposed method. Recognition accuracy is validated and compared with the well-existing methods.

show abstract

“…Fingerprints and finger marks all come together to provide police and courts with the most strong means to identify themselves [6]. The knowledge that the designs and details of the skin of a ridge are unique, immutable, universal, easily classified and leave signs on any matter handled with bare hands is the basis of how it has been a powerful tool [7]. The significance of fingerprints was understood and the detection techniques and operational and strategic applications of fingerprints were investigated.Fingerprints form certain patterns which seem to be of overall form and design similarity [8,9].…”

Section: Introductionmentioning

confidence: 99%

Garber Filter Based Hybridized Algorithm For Feature Extraction From A Fuzzy Fingerprint Image

Devi*

2019

IJEAT

View full text Add to dashboard Cite

Proper extraction of fingerprint functions is important for matching the fingerprint algorithms. Different pieces of fingerprint information, such as rigid orientation and frequency should be taken into consideration for good results. The quality of a fingerprint image is often required to improve the function extraction process. In this article we introduce a Hybridized Garber Filter Algorithm (HGFA) for Fuzzy Fingerprint Image Feature Extraction for effective fingerprint recognition.This paper describes a fingerprint detection system consisting of image preprocessing, filtration, extraction and recognition matching.Preprocessing of images includes normalization based on median value and variation. In order to prepare the fingerprint image further processing, Gabor filters are extracted. The Poincaré index with a partitioning technique is used for the identification of a particular point. The extraction of the ridge line is shown and also the minute extraction with CN algorithm

show abstract

Adaptive fuzzy genetic algorithm for multi biometric authentication

Cited by 34 publications

References 14 publications

Deep rule-based classifier for finger knuckle pattern recognition system

Deep rule-based classifier for finger knuckle pattern recognition system

Efficient method for segmentation of noisy and non‐circular iris images using improved particle swarm optimisation‐based MRFCM

Garber Filter Based Hybridized Algorithm For Feature Extraction From A Fuzzy Fingerprint Image

Contact Info

Product

Resources

About