Multimodal Biometric Algorithm: A Survey

Yahya, Faridah; Nasir, Haidawati; Kushsairy, A. K.; Safie, Sairul Izwan

doi:10.3923/biotech.2016.119.124

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…(7) where, r = The distance between any two fireflies β 0 = The initial attractiveness at r = 0 γ = An absorption coefficient which controls the decrease of the light intensity. The distance that exist in-between any two fireflies i and j, at a particular position xi and xj, can be defined respectively as a Cartesian or Euclidean distance as show below: (8) where, d is the dimensionality of the given problem. The pattern of movement of a particular firefly i that is attracted by another firefly j that is brighter can be represented by the following equation:…”

Section: Eqn (2)mentioning

confidence: 99%

“…A unimodal biometric system uses only one property to perform a recognition operation, such as a fingerprint, nose, gait, voice, face, iris, or ear [7]. However, [8] observed that existing biometric systems have to deal with a variety of problems with the use of a single trait, such as a fingerprint image with a scar or poor illumination of the subject in face recognition. In real-world scenarios, the majority of biometric technologies are unimodal [9].…”

Section: Introductionmentioning

confidence: 99%

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A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

Bukola

2023

int. jour. eng. com. sci

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Many end users are turning to multimodal biometric systems as a result of the limitations of conventional authentication techniques and unimodal biometric systems for offering a high level of accurate authentication. When high accuracy and security are required, multimodal biometrics are the best choice because to the utilization of numerous identification modalities. It is difficult to identify the best features that contribute to the recognition rate/accuracy and have a high redundancy of features since different features are acquired at the feature level fusion from a variety of physiological or behavioral variables. At the feature selection level, the utilization of meta-heuristic algorithms will reduce the number of redundant features while keeping critical feature sets that are important to biometric performance, accuracy, and efficiency. The study demonstrated a multimodal biometric authentication system that used the features of the face and both irises. In order to avoid being stuck at the local optimum and hasten convergence, the Firefly Algorithm (FFA) was modified by including a chaotic sinusoidal map function and a roulette wheel selection mechanism as deterministic processes. The results of the study demonstrated that in terms of sensitivity, precision, recognition accuracy, and time, the proposed MFFA with multimodal outperformed the MFFA for unimodal, bi-modal, and bi-instance. In addition to being computationally faster, more accurate, and suitable for real-time applications, the modified method, known as MFFA, proved effective in integrating multimodal data sets.

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Section: Eqn (2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

Bukola

2023

int. jour. eng. com. sci

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“…The process of PCA transforms image space into Principle Component space [10]. The covariance matrix, Eigenvalues, and Eigenvectors are computed using (1), (2), and (3):…”

Section: A Pcamentioning

confidence: 99%

“…The signature, keystroke, voice, and gait are known the behavioral traits, where the mensuration depends on human action. The mensuration of physical traits depends on human body such as face, iris, hand geometry, and DNA [2].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Biometric System based Face-Iris Feature Level Fusion

Hamd¹,

Mohammed²

2019

IJMECS

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This paper proposed feature level fusion technique to develop a robust multimodal human identification system. The humane face-iris traits are fused together to improve system accuracy in recognizing 40 persons taken from ORL and CASIA-V1 database. Also, low quality iris images of MMU-1 database are considered in this proposal for further test of recognition accuracy. The face-iris features are extracted using four comparative methods. The texture analysis methods like Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are both gained 100% accuracy rate, while the Principle Component Analysis (PCA) and Fourier Descriptors (FDs) methods achieved 97.5% accuracy rate only.

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“…A unimodal biometric system uses only one property to perform a recognition operation, such as a fingerprint, nose, gait, voice, face, iris, or ear [4]. However, [5] observed that existing biometric systems have to deal with a variety of problems with the use of a single trait, such as a fingerprint image with a scar or poor illumination of the subject in face recognition. In real-world scenarios, the majority of biometric technologies are unimodal [6].…”

Section: Introductionmentioning

confidence: 99%

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

T. A.

2023

IJRASET

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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.

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Multimodal Biometric Algorithm: A Survey

Cited by 5 publications

References 5 publications

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

Multimodal Biometric System based Face-Iris Feature Level Fusion

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

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