Biometric security system: a rigorous review of unimodal and multimodal biometrics techniques

Gawande, Ujwalla; Golhar, Yogesh

doi:10.1504/ijbm.2018.10012749

Cited by 7 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A Unimodal biometric system is a system that uses a single source of biometric information to perform authentication. Its mode of operation involves acquisition of biometric data from individuals, performing feature extraction on such data, comparison of extracted features against the template in the database and then making decisions [7,8].The feature set extracted from the acquired biometric data is compared against stored templates in database and a matching score is generated. Using this score, the decision making module either confirms or denies a person's claimed identity.…”

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

“…Using this score, the decision making module either confirms or denies a person's claimed identity. Unimodal biometric systems are often affected by the following limitations [2,3,4,7,8,9,10,11,12,13]. a.…”

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

“…The process of combining the classification results of each biometric channel is known as biometric fusion [18].Fusion in Multimodal biometric systems can be done at different levels. The first is fusion prior to matching and the second is fusion after matching [6,8,12]. Fusion prior to matching occurs before any matching or any use of classifier.…”

Section: Fusion Levelsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

2021

IJATCSE

View full text Add to dashboard Cite

Authentication is a very important aspect of computer security. Most systems employ strategies such as Password-based authentication, Multi-factor authentication, Certificate based authentication which are accompanied with a lot ofchallenges. To address this issue, most security systems have introduced the use of biometrics for authentication. Unimodal biometrics systems have many limitations regarding performance and accuracy. The use of Multimodal biometrics systems for authentication is recently attracting the attention of researchers due to its capacity to overcome most of the drawbacks of Unimodal biometric systems. This paper focuses on the use of biometric technology for authentication. The strengths of multimodal biometric systems, together with the challenges of multimodal biometric systems are presented. The paper also suggests solutions to the challenges of multimodal biometric systems.

show abstract

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Section: Fusion Levelsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

2021

IJATCSE

View full text Add to dashboard Cite

show abstract

“…Hence, the researcher moves towards the region-based deep learning approaches to overcome the existing hand-crafted based system issues [39] [40]. Sliding window schemes are computationally costly and require more time to detect the pedestrian.…”

Section: Related Workmentioning

confidence: 99%

Scale Invariant Mask R-CNN for Pedestrian Detection

Gawande

Hajari²,

Golhar

2020

ELCVIA

View full text Add to dashboard Cite

Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrians helps in various utility applications such as event detection in overcrowded areas, gender, and gait classification, etc. In this domain, the most recent research is based on instance segmentation using Mask R-CNN. Most of the pedestrian detection method uses a feature of different body portions for identifying a person. This feature-based approach is not efficient enough to differentiate pedestrians in real-time, where the background changing. In this paper, a combined approach of scale-invariant feature map generation for detecting a small pedestrian and Mask R-CNN has been proposed for multiple pedestrian detection to overcome this drawback. The new database was created by recording the behavior of the student at the prominent places of the engineering institute. This database is comparatively new for pedestrian detection in the academic environment. The proposed Scale-invariant Mask R-CNN has been tested on the newly created database and has been compared with the pedestrian benchmark databases. The experimental result shows significant performance improvement in pedestrian detection as compared to the existing approaches of pedestrian detection and instance segmentation. Finally, we conclude and investigate the directions for future research.

show abstract

“…Extensive work has been done in the area of biometric recognition using different traits such as face, finger print, iris, voice, different hand-based modalities, gait, etc. [1][2][3][4]. However, unimodal systems have their own advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Deshpande

Mukherji²,

Tavildar³

2019

EURASIP J. on Info. Security

View full text Add to dashboard Cite

A new approach is proposed to quantitatively evaluate the binary detection performance of the biometric personal recognition systems. The importance of correlation between the overall detection performance and the area under the genuine acceptance rate (GAR) versus false acceptance rate (FAR) graph, commonly known as receiver operating characteristics (ROC) is recognized. Using the ROC curve, relation between GAR min and minimum recognition accuracy is derived, particularly for high security applications (HSA). Finally, effectiveness of any binary recognition system is predicted using three important parameters, namely GAR min , the time required for recognition and computational complexity of the computer processing system. The palm print (PP) modality is used to validate the theoretical basis. It is observed that by combining different useful feature-extraction techniques, it is possible to improve the system accuracy. An optimum algorithm to appropriately choose weights has been suggested, which iteratively enhances the system accuracy. This also improves the effectiveness of the system.

show abstract

Biometric security system: a rigorous review of unimodal and multimodal biometrics techniques

Cited by 7 publications

References 0 publications

Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

Scale Invariant Mask R-CNN for Pedestrian Detection

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Contact Info

Product

Resources

About