Person Verification Based on Multimodal Biometric Recognition

Joseph, Annie; Lian, A.; Kipli, Kuryati; Chin, Kho Lee; Mat, Dayang Azra Awang; Voon, Charlie Sia Chin; Ngie, David Chua Sing; Song, Ngu Sze

doi:10.47836/pjst.30.1.09

Cited by 4 publications

(2 citation statements)

References 26 publications

(36 reference statements)

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“…The face and fingerprint features are fused in match score-level fusion based on a convolution neural network (CNN). The multimodal biometric system is evaluated on the University of California Irvine (UCI) machine learning repository and the system achieved promising human recognition results [7].…”

Section: Related Workmentioning

confidence: 99%

Philosophy design of single-trait based multi-feature biometric system

A. Rasool,

Hamd

2024

TELKOMNIKA

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This paper presents new techniques for designing a simple and reliable multifeatured biometric system based on a single trait source. First, a one-toone relationship between the feature's edge and its associated angle is utilized after extracting the contrast feature using the gray-level co-occurring matrix (GLCM) method. Secondly, the classifying stage is modified to process one-dimensional vectors rather than the whole feature's template. That means whatever the template size is, the matching operation is always processing a one-dimensional vector called a mean-feature vector which requires low storage and less computation complexity. Finally, for comparison purposes, the performances of the three biometric systems are calculated for recognizing 170 subjects taken from four facial databases. These comparisons are made using three error distance measurements. The recognition rates of the angle-based feature were very competitive to the regular edge-based results; however, the overall recognition accuracy is highly improved after fusing the decision of the two unibiometric systems using the Logic-OR operator. The fused system performance was satisfactory and it shows that the decision fusion of the single source trait based multifeatured system has promising performance represented by accuracy improvement, low storage, and low matching time.

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Section: Related Workmentioning

confidence: 99%

Philosophy design of single-trait based multi-feature biometric system

A. Rasool,

Hamd

2024

TELKOMNIKA

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“…In deep learning, neural networks with multiple layers are generally referred to as deep neural networks, which illustrate how neural networks with multiple layers can successfully create representational structures. [32][33][34]. Te weights of these networks can be adjusted using feature learning algorithms with and without observers.…”

Section: Deep Learningmentioning

confidence: 99%

Deep Hybrid Multimodal Biometric Recognition System Based on Features‐Level Deep Fusion of Five Biometric Traits

Safavipour

Doostari

2023

Computational Intelligence and Neuroscience

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The need for information security and the adoption of the relevant regulations is becoming an overwhelming demand worldwide. As an efficient solution, hybrid multimodal biometric systems utilize fusion to combine multiple biometric traits and sources with improving recognition accuracy, higher security assurance, and to cope with the limitations of the uni-biometric system. In this paper, three strategies for dealing with a feature-level deep fusion of five biometric traits (face, both irises, and two fingerprints) derived from three sources of evidence are proposed and compared. In the first two proposed methodologies, each feature vector is mapped from the feature space into the reproducing kernel Hilbert space (RKHS) separately by selecting the appropriate reproducing kernel. In this higher space, where the result is the conversion of nonlinear relations to linear ones, dimensionality reduction algorithms (KPCA, KLDA) and quaternion-based algorithms (KQPCA, KQPCA) are used for the fusion of the feature vectors. In the third methodology, the fusion of feature spaces based on deep learning is administered by combining feature vectors in in-depth and fully connected layers. The experimental results on 6 databases in the proposed hybrid multibiometric system clearly show the multimodal template obtained from the deep fusion of feature spaces; while being secure against spoof attacks and making the system robust, they can use the low dimensionality of the fused vector to increase the accuracy of a hybrid multimodal biometric system to 100%, showing a significant improvement compared with uni-biometric and other multimodal systems.

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A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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Person Verification Based on Multimodal Biometric Recognition

Cited by 4 publications

References 26 publications

Philosophy design of single-trait based multi-feature biometric system

Philosophy design of single-trait based multi-feature biometric system

Deep Hybrid Multimodal Biometric Recognition System Based on Features‐Level Deep Fusion of Five Biometric Traits

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

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