Cascaded multimodal biometric recognition framework

Baig, Asim; Bouridane, Ahmed; Kurugöllü, Fatih; Albesher, Badr

doi:10.1049/iet-bmt.2012.0043

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…Multimodal identification procedure is developed by integrating data from several biometric sources. Baig et al [19] suggested a cascaded classifier-based model for utilization in biometrics recognition. The suggested model uses a set of weak classifiers for decreasing registered users' database to a smaller set of candidate users.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Biometric Identification System using Deep Learning

D.N.,

H.K.

2020

IJSRCSE

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In real world we knows that a multimodal biometric system performs better and overcomes the limitation and gives better classification accuracy when compare to than Unimodal biometric system. This paper proposes a novel multimodal biometric recognition system based on feature-level fusion and deep learning model. The significance of this paper is, it focuses on the issue of selection of best feature extraction and classification techniques, by investigating different types of feature extraction techniques with different databases of given modality like face, Plamprint and iris. We proposed unimodal biometric recognition using Convolution Neural Network (CNN). Later the results of unimodal recognition used two-layer fusion to build multimodal biometric recognition. Features like Historgram of Gradient, Zernike Moments and Pseudo Zernike Moments are extracted. The performance of proposed multimodal recognition method shows better recognition accuracy than unimodal recognition.

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Section: Related Workmentioning

confidence: 99%

Multimodal Biometric Identification System using Deep Learning

D.N.,

H.K.

2020

IJSRCSE

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“…ii. As a result, interdisciplinary researchers, scientists and various research committees try to mitigate the effect of covariates challenges of the face image of animals using computer vision approaches [74][75][76]. The effects of covariates are significant problems for representation, detection and classification of species or individuals.…”

Section: Fundamental Requirements For Promising Applicationsmentioning

confidence: 99%

Visual animal biometrics: survey

Kumar

Singh

2017

IET biom.

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Visual animal biometrics is an emerging research discipline in computer vision, pattern recognition and cognitive science. It is a promising research field that encourages new development of quantified algorithms and methodologies for representing, detection of visible features, phenotypic appearances of species, individuals and recognition of morphological and animal biometric characteristics. Furthermore, it also assists the study of animal trajectory and behaviours analysis of species. Currently, real-world applications of visual animal biometric systems are gaining more proliferation due to a variety of applications and use, enhancement of quantity and quality of the collection of extensive ecological data and processing. However, to advance visual animal biometrics will require integration of methodologies among the scientific disciplines involved. Such valuable efforts will be worthwhile due to the enormous perspective of this approach rests with the formal abstraction of phenomics, to build well-developed interfaces between different organisational levels of life. This study provides a comprehensive survey of visual animal biometric systems and recognition approaches for various species and individual animal based on their morphological image pattern and biometric characteristics. This comprehensive review paper encourages the multidisciplinary researchers, scientists, biologists and different research communities to design the better platforms for the development of efficient algorithms and learning models to solve the massive data processing, classification and identification of different species related problems.

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Cascade‐based Multimodal Biometric Recognition System with Fingerprint and Face

Tomar

Singh

2021

Macromolecular Symposia

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In recent years, biometrics has played a vital role in protecting users' privacy and enabling secure authentication. Multimodal security, comprised of identity cards with attached unique passwords, is used to authenticate the genuine or imposter person; however, it is not a perfect security framework. Biometric traits are unique to every individual and hence proved to be very secure. This paper proposes a hybrid approach by combining cascaded and fusion‐based multimodal biometric framework using fingerprint and face traits. The fingerprint and face features are extracted using the minutiae feature extraction algorithm and principal component analysis (PCA) algorithm. The hybrid approach is applied to a self ‐built database of around 450 fingerprints and 450 face images. The performance of the proposed hybrid system is determined using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Accuracy evaluation parameters. The unimodal system of fingerprint and face at Level I and Level II yields an accuracy of 100% at a threshold value of 0.42 and 0.54, respectively. The multimodal system at Level III delivers an accuracy rate of 93.70%, 99.20%, 97.85%, and 99.50% at a threshold value of 0.75, 0.60, 0.30, and 0.60 in sum level fusion, product level fusion, min level fusion, and max level fusion schemes, respectively.

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Cascaded multimodal biometric recognition framework

Cited by 9 publications

References 18 publications

Multimodal Biometric Identification System using Deep Learning

Multimodal Biometric Identification System using Deep Learning

Visual animal biometrics: survey

Cascade‐based Multimodal Biometric Recognition System with Fingerprint and Face

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