Secured Multi Modal Biometric System : A Review

Akulwar, Pooja; Vijapur, Nataraj

doi:10.1109/i-smac47947.2019.9032628

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…In [32], [33], [34], and [35], they presented a survey on activity recognition leveraging fusion techniques. [36] and [37] presented a survey on biometric systems where fusion techniques were applied to merge multiple data. Fusion is commonly used in audio-visual information fusing.…”

Section: Related Studiesmentioning

confidence: 99%

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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Section: Related Studiesmentioning

confidence: 99%

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

2023

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“…However, each process is witnessed with certain pitfalls [12]. At present, there is also the prevalence of using multi-modalities for user authentication systems in the perspective of biometrics [13]. However, they have their own boundaries of effectiveness that is specifically meant for application.…”

Section: Introductionmentioning

confidence: 99%

Novel Optimization of Identified Palm Geometry Using Image Segmentation

Mahalakshmi¹,

Sheela²

2022

Int. J. Onl. Eng.

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Segmentation is one of the essential steps towards the identification of any object in the domain of image processing. In the area of hand-based biometric which is mainly deployed for a user authentication system, segmentation plays a critical role. A review of existing studies shows that there is a very less amount potential contribution in this regard. Therefore, this manuscript presents a novel optimization scheme towards palm geometry recognition system where segmentation process is the prime highlights for classification of hand and background considering a case study of finger recognition. Further, the proposed scheme uses masking operation where the Region-of-Interest section of hand is subjected to segmentation. Further proposed system uses machine learning approach (convolution neural network and Siamese Neural Network) to further assist in optimizing the segmentation performance. The experimental outcome of the study shows proposed system offers better accuracy compared to the existing system.

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“…statement is to ascertain a potential feature extraction while analyzing hand geometry because of the variable nature of the hand geometry in perspective of different modalities. In the field of biometrics, there is a trend towards multiple approaches to user authentication systems [12], [13]. However, they have a lot of obvious limitations on applications.…”

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confidence: 99%

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

Shivabasappa

Sheela²

2023

IJECE

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<span lang="EN-US">Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden.</span>

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Secured Multi Modal Biometric System : A Review

Cited by 7 publications

References 12 publications

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

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

Novel Optimization of Identified Palm Geometry Using Image Segmentation

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

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