New chaff point based fuzzy vault for multimodal biometric cryptosystem using particle swarm optimization

Amirthalingam, Gandhimathi; Radhamani, G.

doi:10.1016/j.jksuci.2014.12.011

Cited by 13 publications

(9 citation statements)

References 18 publications

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“… [ 85 ] IITK contains 801 side face images, UND-J2 contains 1800 side face images Ear+ Side Face SURF feature fusion of ear and profile face IITK: 99.36, UND-E: 98.02, and UND-J2: 96.02 3. [ 86 ] Face—Yale face database of 165 images and IIT Delhi ear database and 471 images ear images Ear+Face Particle swarm optimisation 90 4. [ 87 ] IITdelhi-2 ear, IITD Delhi palm print database Ear+ Palm print Local texture descriptor 100 5.…”

Section: Taxonomic Review On Ear Biometricmentioning

confidence: 99%

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A comprehensive survey and deep learning-based approach for human recognition using ear biometric

2021

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Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.

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Section: Taxonomic Review On Ear Biometricmentioning

confidence: 99%

“…Moreover, it has been identified that the score-level fusion performed is better than the feature level. [ 86 ] proposed particle swarm optimisation (PSO)-based method. The technique was applied on the Face–Yale face database of 165 greyscale images, the IIT Delhi ear database, and 471 image ear images.…”

Section: Taxonomic Review On Ear Biometricmentioning

confidence: 99%

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

2021

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“…It is obvious that ( ) = V( Θ ( ) , Τ ( ) , ) Υ ( ) is least square solution of (12). and technology innovation program of Chongqing University of Science & Technology (YKJCX1720407), and in part by the Chongqing Outstanding Youth Fund Project (cstc2014jcyjjq90002).…”

Section: Cmentioning

confidence: 99%

“…In BCs, a key is either bound (key binding schemes) or extracted (key generation schemes) from biometric data [8]. The key binding schemes can be classified as fuzzy vault schemes [12] and the fuzzy commitment schemes [13]. Regarding key generation schemes, a number of discretized fingerprint descriptors and appropriate error correcting codes were used to propose an effective biometric cryptosystem construction for biometric template protection 2 Complexity in [14].…”

Section: Introductionmentioning

confidence: 99%

Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Han

Deng

et al. 2018

Complexity

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In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

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“…A fused multimodal system like FLF is used over the fingerprint, retina and finger vein to reduce the genuine acceptance rate (GAR) and also the security against spoofing is improved [6]. A chaff point based fuzzy vault extracts the features of face and ear points and the best point is created by selecting the best locations of feature vectors which is found by the particle swarm optimization (PSO) [7]. The fusion over the face and voice modules made by the transformation based fusion algorithm over an ensemble classifier and also the fusion of these behaviour traits are made by Adaptive Neuro Fuzzy Inference System (ANFIS) [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Biometric: Bi-Directional Empirical Mode Decomposition with Hilbert-Hung Transformation

Gavisiddappa¹,

Mohan²,

Mahadevappa³

et al. 2019

IJACSA

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Biometric systems (BS) helps in reorganization of individual person based on the biological traits like ears, veins, signatures, voices, typing styles, gaits, etc. As, the Uni-modal BS does not give better security and recognition accuracy, the multimodal BS is introduced. In this paper, biological characters like face, finger print and iris are used in the feature level fusion based multimodal BS to overcome those issues. The feature extraction is performed by Bi-directional Empirical Mode Decomposition (BEMD) and Grey Level Co-occurrence Matrix (GLCM) algorithm. Hilbert-Huang transform (HHT) is applied after feature extraction to obtain local features such as local amplitude and phase. The combination of BEMD, HHT and GLCM are used for achieving effective accuracy in the classification process. MMB-BEMD-HHT method is used in Multiclass support vector machine technique (MC-SVM) as a classifier. The false rejection ratio has improved using feature level fusion (FLF) and MC-SVM technique. The performance of MMB-BEMD-HHT method is measured based on the parameters like False Acceptance Ratio (FAR), False Rejection Ratio (FRR), and accuracy and compared it with an existing system. The MMB-BEMD-HHT method gave 96% of accuracy for identifying the biometric traits of individual persons.

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New chaff point based fuzzy vault for multimodal biometric cryptosystem using particle swarm optimization

Cited by 13 publications

References 18 publications

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Multi-Modal Biometric: Bi-Directional Empirical Mode Decomposition with Hilbert-Hung Transformation

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