Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

Sim, Hiew Moi; Asmuni, Hishammuddin; Hassan, Rohayanti; Othman, Razib M.

doi:10.1016/j.eswa.2014.02.051

Cited by 100 publications

(39 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scheme is based on RBF (radial basis function) neural network fusion rules and applies both transformation-based score and classifier-based score fusion strategies. A new method has been proposed in [20] to fuse face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weights availability. A more recent scheme has been proposed by [21], which uses matching score level and feature level fusion combination to improve the face and iris multimodal biometric systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

View full text Add to dashboard Cite

Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

View full text Add to dashboard Cite

show abstract

“…It is sure that weighted score level fusion can better take advantages of data from different sources [23][24][25][26][27]. However, it is hard to determine optimal weights for weighted score level fusion.…”

Section: Introductionmentioning

confidence: 98%

Adaptive weighted fusion: A novel fusion approach for image classification

2015

Neurocomputing

View full text Add to dashboard Cite

“…There are two main types of biometrics: physical (e.g., a fingerprint, face, iris, or hand) and behavioral (e.g., a handwritten signature or keyboard dynamics such as rhythm, speed, and use of the left or right shift key). Recently, much research has been conducted to develop models to combine several biometrics for user authentication [1][2][3][4]. In comparison to previous non-biometric-based authentication systems, biometric-based systems do not require the user to remember passwords or possess security tokens.…”

Section: Introductionmentioning

confidence: 99%

Construction of a New Biometric-Based Key Derivation Function and Its Application

Seo

Park

Kim

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

Biometric data is user-identifiable and therefore methods to use biometrics for authentication have been widely researched. Biometric cryptosystems allow for a user to derive a cryptographic key from noisy biometric data and perform a cryptographic task for authentication or encryption. The fuzzy extractor is known as a prominent biometric cryptosystem. However, the fuzzy extractor has a drawback in that a user is required to store user-specific helper data or receive it online from the server with additional trusted channel, to derive a correct key. In this paper, we present a new biometric-based key derivation function (BB-KDF) to address the issues. In our BB-KDF, users are able to derive cryptographic keys solely from their own biometric data: users do not need any other user-specific helper information. We introduce a security model for the BB-KDF. We then construct the BB-KDF and prove its security in our security model. We then propose an authentication protocol based on the BB-KDF. Finally, we give experimental results to analyze the performance of the BB-KDF. We show that our proposed BB-KDF is computationally efficient and can be deployed on many different kinds of devices.

show abstract

Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

Cited by 100 publications

References 21 publications

Optimal Face-Iris Multimodal Fusion Scheme

Optimal Face-Iris Multimodal Fusion Scheme

Adaptive weighted fusion: A novel fusion approach for image classification

Construction of a New Biometric-Based Key Derivation Function and Its Application

Contact Info

Product

Resources

About