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Machine Learning Based Robust Access for Multimodal Biometric Recognition
2020
International journal of recent technology and engineering
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For every biometric we used separately feature extraction techniques and we combined those features in efficient way to get robust combination. ...
The algorithms are kernelised so as to handle nonlinear data efficiently. The result of the proposed system is compared to already existing image fusion methods to show its advantage over them. ...
There is also an algorithm for face recognition which is Sparse Representation -based Classification (SRC). ...
doi:10.35940/ijrte.f2374.018520
fatcat:oav7tky7ujedzkf275lrvdxkxe
Sparse representations and Random Projections for robust and cancelable biometrics
2010
2010 11th International Conference on Control Automation Robotics & Vision
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In this paper, we review the role of sparse representation and CS for efficient biometric identification. Algorithms to perform identification from face and iris data are reviewed. ...
Some of the most compelling challenges and issues that confront research in biometrics using sparse representations and CS are also addressed. ...
Section III presents the sparse representation-based classification algorithm and discuss some of the recognition results on face and iris biometrics. ...
doi:10.1109/icarcv.2010.5707955
dblp:conf/icarcv/PatelCT10
fatcat:tccdsqwxird6jbkeuk2dvx4gue
Joint Sparse Representation for Robust Multimodal Biometrics Recognition
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Furthermore, we also kernelize the algorithm to handle non-linearity in data. The optimization problem is solved using an efficient alternative direction method. ...
We propose a novel multimodal multivariate sparse representation method for multimodal biometrics recognition, which represents the test data by a sparse linear combination of training data, while constraining ...
[10] proposed a robust sparse representation-based classification (SRC) algorithm for face recognition. ...
doi:10.1109/tpami.2013.109
pmid:24231870
fatcat:wty5whbbvjftpck2garb5sjlay
Multimodal Biometrics Recognition by using Modified Unconstrained Cohort Normalisation under Unconstrained Setting
2015
Indian Journal of Science and Technology
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Methods: Method used in this research is pattern recognition algorithm namely modified unconstrained cohort normalisation (MUCN) is introduced into the score-level fusion process of multi-biometric system ...
Results: The result of presented pattern recognition algorithm performs well in terms of recognition accuracy when compared to existing schemes.From the comparative evaluation on WVU multimodal data set ...
Then in 11 the seminal sparse representation-based classification (SRC) algorithm is presented by Wright et al. for face recognition. ...
doi:10.17485/ijst/2015/v8i34/70767
fatcat:xszszprtqrbenmpqew232a667m
Human Face Identification based on Optimal Sparse Features
2019
International Journal of Engineering and Advanced Technology
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The effect of change in occlusion can be easily addressed by using this optimal sparse representation based classification (OSRC) algorithm. ...
Recent advancement in security is the user authentication using face recognition. But the flaws in existing face recognition systems are yet to be addressed. ...
Optimal sparse representation based classification (OSRC) algorithm provides a recognition rate [10] of 99.17%. ...
doi:10.35940/ijeat.b3098.129219
fatcat:p4t2vfx5djfall5bgvcpopiqxu
Through Biometric Card In Romania: Person Identification By Face, Fingerprint And Voice Recognition
2008
Zenodo
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Face recognition uses parts-based representation methods and a manifold learning approach. The assessment criterion is recognition accuracy. ...
In this paper three different approaches for person verification and identification, i.e. by means of fingerprints, face and voice recognition, are studied. ...
INTRODUCTION IOMETRIC recognition refers to the use of distinctive physiological (e.g., fingerprints, face, retina, voice) and behavioral (e.g., gait, signature) characteristics, called biometric identifiers ...
doi:10.5281/zenodo.1071629
fatcat:tlmrpussunbnddfnzm3auoufae
Joint Sparsity-Based Robust Multimodal Biometrics Recognition
[chapter]
2012
Lecture Notes in Computer Science
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We propose a novel multimodal multivariate sparse representation method for multimodal biometrics recognition, which represents the test data by a sparse linear combination of training data, while constraining ...
While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received ...
Our algorithm for multimodal biometrics recognition is summarized in Algorithm 2. Algorithm 2: Sparse Multimodal Biometrics Recognition (SMBR). ...
doi:10.1007/978-3-642-33885-4_37
fatcat:uu6bl7756zhvdjbidnd7gjixne
SPARSE REPRESENTATION THEORY AND ITS APPLICATION FOR FACE RECOGNITION
2015
International Journal on Smart Sensing and Intelligent Systems
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Sparse representation based classification for face images has been one of efficient approaches for face recognition in recent years. ...
Discrimination performance by using the sparse representation can also be applied to the face recognition, and any test sample can be expressed as a linear span of the all training samples. ...
To the characteristics of noncontact recognition method can get face image using visible information, which is different from fingerprint or iris recognition, fingerprint requires the use of electronic ...
doi:10.21307/ijssis-2017-751
fatcat:pmththpavvbvxou7t254of74iy
Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture
2021
IEEE Access
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Fingerprint recognition systems have been applied widely to adopt accurate and reliable biometric identification between individuals. ...
Four datasets of fingerprint images have been used to prove the robustness of the proposed architecture. The dataset of fingerprint images has been collected from various real resources. ...
[41] explored face recognition with deep learning. ...
doi:10.1109/access.2021.3124746
fatcat:twzkbnr2lzafnnokw3oq6o4jua
A Sparse Representation of Complete Local Binary Pattern Histogram for Human Face Recognition
[article]
2016
arXiv
pre-print
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Human face recognition has been a long standing problem in computer vision and pattern recognition. ...
On the other hand, having the histograms of the considered images extracted, we exploit their sparsity to construct a so-called Sparse Representation Classifier (SRC) for further face classification. ...
], smart gun [17] , fingerprint identification [18] , automated cell phenotype image classification [19] and others. ...
arXiv:1605.09584v1
fatcat:ot3inuwkq5eqxhkok4i4dqp3ou
Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint
2015
International Journal of Modern Education and Computer Science
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Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is ...
The proposed system has proven its remarkable ability to overcome the limitations of unimodal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. ...
ZM algorithm brings us these benefits: ZM algorithm enables a face-fingerprint recognition system to work on images of various shapes as its performance is based on the identified center of the image ...
doi:10.5815/ijmecs.2015.05.02
fatcat:lnwzdex77vg5fdzuasrj4yiove
Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images
2020
PeerJ Computer Science
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In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. ...
In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. ...
Huang et al. (2015) introduced an adaptive bimodal sparse representation based on classification, that is, adaptive face and ear using bimodal recognition system based on sparse coding, where the qualities ...
doi:10.7717/peerj-cs.248
pmid:33816900
pmcid:PMC7924518
fatcat:isdm5wkcqnhlxgjtyqt3nwkavu
SURVEY ON FUSION OF MULTIMODAL BIOMETRICS USING SCORE LEVEL FUSION
2016
ELK Asia Pacific Journal of Computer Science and Information Systems
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Depending on the classification result of the 2ν-GSV classifier, an appropriate fusion algorithm is used to evaluate the fused match score, of evidence is used in order to combine face and voice modalities ...
-GSVM classifier is used to classify the input probe data. The classification algorithm selects either the evidence-theoretic DSm or sum rule fusion to fuse the probe match scores. ...
Jain, "Integrating faces and fingerprints for personal identification," IEEE Trans. Pattern Anal. ...
doi:10.16962/eapjcsis/issn.2394-0441/20160930.v2i1.06
fatcat:d6alyseolvhr7pdqoma5ih75ve
Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images
2019
Automatika
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This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris. ...
Out of these comparisons, the multimodal face, fingerprint and iris fusion offers significant improvements in the recognition rate of the suggested multimodal biometric system. ARTICLE HISTORY ...
Lastly, deep learning template matching algorithm is used for classification to calculate the recognition accuracy. ...
doi:10.1080/00051144.2019.1565681
fatcat:ziqkw7iw6veabakdsoo5j7vh3i
A Novel Approach for Fingerprint Sparse Coding Analysis using K-SVD Learning Technique
2015
International Journal of Engineering Research and
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This algorithm is based on sparse representation. ...
In biometrics it can be classified into several types, they are face, fingerprint, retina, hand geometry etc. In this paper they introduced a new fingerprint compression algorithm. ...
A general classification algorithm for object recognition based on a sparse representation computed by l 1 -minimization is proposed by the author [13] . ...
doi:10.17577/ijertv4is030726
fatcat:yd23psq4qbau5hma46qba634ha
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