Feature level fusion approach for personal authentication in multimodal biometrics

Evangelin, L. Nisha; Fred, A. Lenin

doi:10.1109/iconstem.2017.8261272

Cited by 6 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study's computing complexity was extremely low, and it can be used in real-time applications. Finger print, palm and finger knuckle prints were fused at the feature level for personal authentication in [28] study. The distinctive properties of these Modalities are extracted using the Grey Level Co Occurrence Matrix (GLCM) feature extraction technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Firefly and Modified Firefly Algorithms in Multimodal Biometric Authentication Systems

T. A.

2023

IJRASET

View full text Add to dashboard Cite

This research focuses on addressing the challenges of high-dimensional feature spaces and selecting significant features in multimodal biometric systems. Feature-level fusion poses persistent issues that require extensive investigation due to its potential for enhancing biometric recognition accuracy. This study proposes the integration of meta-heuristic optimization techniques into the feature selection phase of a multimodal biometric system, prior to the classification phase, to identify the most relevant features from two distinct biological traits. To determine the authorization status of an individual, the fused feature vectors are inputted into a Support Vector Machine (SVM) classifier. The study leverages the physiological biometrics of the face and iris to validate the findings of previous research, highlighting the exceptional accuracy of iris recognition and the natural acceptability of face recognition for identity verification purposes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Firefly and Modified Firefly Algorithms in Multimodal Biometric Authentication Systems

T. A.

2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…In their research, Evangelin et al [ 40 ] proposed a multimodal biometric framework utilizing unique finger impression and iris. Unmistakable printed components of the iris and finger impression are extricated utilizing the Haar wavelet-based strategy.…”

Section: An Overview Of Related Researchmentioning

confidence: 99%

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Srivastava

Bhardwaj

Othman

et al. 2022

Sensors

View full text Add to dashboard Cite

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

show abstract

“…Reference [19] introduced a biometric system that integrates face and FP. Evangelin and Fred [20] proposed the fusion identification frame of FP, palm print and FKP on feature level. In 2016, Khellat-Kihel et al [12] proposed a recognition framework for the fusion of trimodal biometrics on the finger on the feature and decision level.…”

Section: Introductionmentioning

confidence: 99%

Graph Fusion for Finger Multimodal Biometrics

Zhang

Shi

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In terms of biometrics, a human finger itself is with trimodal traits including fingerprint, finger-vein, and finger-knuckle-print, which provides convenience and practicality for finger trimodal fusion recognition. The scale inconsistency of finger trimodal images is an important reason affecting effective fusion. It is therefore very important to developing a theory of giving a unified expression of finger trimodal features. In this paper, a graph-based feature extraction method for finger biometric images is proposed. The feature expression based on graph structure can well solve the problem of feature space mismatch for the finger three modalities. We provide two fusion frameworks to integrate the finger trimodal graph features together, the serial fusion and coding fusion. The research results can not only promote the advancement of finger multimodal biometrics technology but also provide a scientific solution framework for other multimodal feature fusion problems. The experimental results show that the proposed graph fusion recognition approach obtains a better and more effective recognition performance in finger biometrics.

show abstract

Feature level fusion approach for personal authentication in multimodal biometrics

Cited by 6 publications

References 3 publications

Comparative Analysis of Firefly and Modified Firefly Algorithms in Multimodal Biometric Authentication Systems

Comparative Analysis of Firefly and Modified Firefly Algorithms in Multimodal Biometric Authentication Systems

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Graph Fusion for Finger Multimodal Biometrics

Contact Info

Product

Resources

About