An Efficient Human Identification through MultiModal Biometric System

Meena, K.; Malarvizhi, N.

doi:10.1590/1678-4324-2016161055

Cited by 8 publications

(9 citation statements)

References 4 publications

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“…This is also in agreement with observations in Chapter 4. Classification results for case 2 in which the sheep class composition included subjects [1,3,5,7,9,11,13,15,17,19,21] and wolves class composition included subjects [2,4,6,8,10,12,14,16,18,20,22] are shown in Tables 5.5, 5.6 and 5.7. The best accuracy for this case was obtained using the RBF kernel (90.91%) which was for the Retest/Test scenario of the highest computational resolution spectrogram (window size of 512 samples and overlap size of 511 samples).…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Based Listener Classification and Authentication Using Frequency Following Responses to English Vowels for Biometric Applications

Borzou,

Bouchard,

Dajani

2023

2023 IEEE Sensors Applications Symposium (SAS)

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Auditory Evoked Potentials (AEPs) have recently gained attention as a biometric feature that may improve security and address reliability shortfalls of other commonly-used biometric features.The objective of this thesis is to investigate the accuracy with which subjects can be automatically identified or authenticated with machine learning (ML) techniques using a type of AEP known as the speech-evoked frequency following response (FFR).Accordingly, the results show more accurate discrimination between FFRs from different subjects than what has been reported in past studies. The accuracy improvement is searched either by optimized hyperparameter tuning of the ML model or extracting new features from FFRs and feeding them as inputs to the model. Finally, the accuracy of authenticating subjects using FFRs is investigated using a "sheep vs. wolves" scenario.The results of this work shed more light on the potential of use of speech-evoked FFRs in biometric identification and authentication systems.Deciding to pursue a second M.Sc. program after completing my PhD was a turning point in my life and professional career, which I will always remember. Moving forward on this path was impossible without the support and help of my supervisors. Therefore, I would like to express my sincere gratitude to Dr. Martin Bouchard and Dr. Hilmi Dajani for giving me this opportunity and guiding my research. Their fantastic guidance and cooperation throughout my M.Sc. studies were invaluable.Once again, similar to my previous Ph.D., M.Sc., and B.A.Sc. theses, I would like to express my heartfelt thanks to my best friend forever, Mohammad Alavirad, whom I cannot call anything but my brother. His help, presence, and dedication during the last ten years of my life cannot be expressed in words. Without him, it would have been impossible to overcome the challenges and complete this long journey.My parents have played an essential role in helping me achieve our dreams. I am blessed to have parents who have supported and encouraged me during the lowest of lows and the highest of highs, fueling my every moment.

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Section: Resultsmentioning

confidence: 99%

“…By employing a support vector machine classifier, their approach achieved impressive accuracies of 99.1% and 90.8%, respectively. Finally, for biometric identification using iris recognition, accuracies as high as 99.5% are reported in the literature [9].…”

Section: Biometric Identification and Authentication Techniquesmentioning

confidence: 99%

Machine Learning Based Listener Classification and Authentication Using Frequency Following Responses to English Vowels for Biometric Applications

Borzou,

Bouchard,

Dajani

2023

2023 IEEE Sensors Applications Symposium (SAS)

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“…Finally, SVM and NN are used to classify the data. To test the proposed FR solution, the datasets ORL, GT, JAFFE, Yale, YB, and EYB were supplied [16]. A new masked face recognition strategy has been proposed, combining a block-based strategy with a ground-breaking attention block (CBAM).…”

Section: Literature Reviewmentioning

confidence: 99%

Descriptor feature based on local binary pattern for face classification

Sahy¹

2022

PEN

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Local Binary Patterns (LBP) is a non-parametric descriptor whose purpose is to effectively summarize local image configurations. It has generated increasing interest in many aspects including facial image analysis, vision detection, facial expression analysis, demographic classification, etc. in recent years and has proven useful in various applications. This paper presents a local binary pattern based face recognition (LBP) technology using a Vector Support Machine (SVM). Combine the local characteristics of LBP with universal characteristics so that the general picture characteristics are more robust. To reduce dimension and maximize discrimination, super vector machines (SVM) are used. Screened and Evaluated (FAR), FARR and Accuracy Score (Acc), not only on the Yale Face database but also on the expanded Yale Face Database B datasets, the test results indicate that the approach is accurate and practical, and gives a recognition rate of 98 %.

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“…The best GAR achieved was 98.9%. In another research, the features from face, fingerprints and iris were fused together by Meena et al [23]. Authors used a dataset of 280 subjects.…”

Section: Related Workmentioning

confidence: 99%

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

et al. 2021

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The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

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An Efficient Human Identification through MultiModal Biometric System

Abstract: Human identification is essential for proper functioning of society. Human identification through multimodal

Cited by 8 publications

References 4 publications

Machine Learning Based Listener Classification and Authentication Using Frequency Following Responses to English Vowels for Biometric Applications

Machine Learning Based Listener Classification and Authentication Using Frequency Following Responses to English Vowels for Biometric Applications

Descriptor feature based on local binary pattern for face classification

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

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