Features extraction of fingerprints using firefly algorithm

Al-Ta'l, Ziyad Tariq Mustafa; Abdulhameed, Omar Younis

doi:10.1145/2523514.2527014

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Principle Component Analysis (PCA) and Fourier Descriptors (FDs) approaches obtained 97.5 percent accuracy, while Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) texture analysis techniques reached 100% accuracy. The performance of multimodal biometric identification was improved by merging fingerprint and iris at the feature level fusion (25). Multimodal biometrics' security phase was made possible by the method of fused picture encryption using the AES algorithm and matching to validate the encryption.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

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

T. A.

2023

IJRASET

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“…Paper [2] proposed the Firefly algorithm (FA) as a method to extract features from the image. This algorithm mimics flashing behavior of fireflies in nature.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…However, cards or keys oftentimes lost or be stolen as well as passwords oftentimes forgotten or revealed. For more reliable identification or verification, it is necessary to use something that truly determines the person [2].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Features extraction of fingerprints based on hybrid particle swarm optimization and bat algorithms

Ahmed

Hassoon

AL.hak

et al. 2022

EEJET

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Most security system's essential errand is to check that the people are in fact who they claim to be. In Contrast to traditional techniques such as passwords and smart cards that are used in some organizations, fingerprint identification may be preferred as it makes the information virtually impossible to steal. The most extensive used biometric features are Fingerprints, in order to identify a person because of their uniqueness and invariance. The fingerprint consists of valleys and ridges on the surface of a fingertip. In this paper, a new hybrid strategy Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed to extract features from fingerprint images. Both PSO and BA algorithms are swarm-based algorithms that mimics the swarm behaviour of particles and bats in nature. In the field of image processing, features are extremely significant. Before obtaining features, the noisy area should be removed from the foreground first, and then several important techniques are applied on each sample image in the database such as Fingerprint Enhancement by using Fast Fourier Transform (FFT), Binarization, and Thinning. The hybrid (PSO-BA) algorithm is proposed as a pre-enhancing step to select the clear minutiae (or feature) structures across several iterations, which will be more suited for the matching phase. By comparing the proposed method with several methods in calculating FAR and FRR, the results showed that the FAR (0.001) and FRR (0.01) were less than the other proposed methods. That means the hybrid (PSO-BA) algorithm has the better results, which means it can be used as one of the best search approaches to extract features from fingerprints

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“…In (Li, Kamlesh, Lim, & Neoh, 2017), the Firefly optimization was implemented for feature selection with the combination of classification and regression models. Moreover, FFA obtained some successful results in fingerprint feature extraction (Tariq, Al-Ta'i, & Abdulhameed, 2013).…”

Section: Introductionmentioning

confidence: 97%

Saldırı Tespiti için Ateş Böceği Algoritması Tabanlı Özellik Seçim Yöntemi ve Yapay Bağışıklık Sistemi

Gunay¹,

Orman²

2020

Uludağ University Journal of the Faculty of Engineering

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Intrusion detection systems generally produce high dimensional data in network-based computer systems. It is required to analyze this data effectively and create a successful model by selecting the important features to save only the meaningful data and protect the system against suspicious behaviors and attacks that can occur in a system. Firefly Algorithm (FFA) is one of the most promising meta-heuristic methods which can be used to select important features from big data. In this paper, a modified Firefly Algorithm-based feature selection method is proposed. The traditional Firefly Algorithm is improved by using the K-Nearest Neighborhood (K-NN) classifier and an additional feature selection step. The proposed method is tested on 4 different datasets of various types of attacks. Three different sub-feature sets are obtained for each dataset and the classification performances are compared. Artificial Immune System (AIS) method is also implemented to generate artificial data for the datasets that have an insufficient number of data. This study shows that the proposed Firefly Algorithm performs successfully to decrease the dimension of data by selecting the features according to the obtained accuracy rates of the K-NN method. Memory usage is dramatically decreased over 50% by reducing the dimension with the proposed FFA. The obtained results indicate that this method both saves time and memory usage.

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Features extraction of fingerprints using firefly algorithm

Cited by 4 publications

References 4 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

Features extraction of fingerprints based on hybrid particle swarm optimization and bat algorithms

Saldırı Tespiti için Ateş Böceği Algoritması Tabanlı Özellik Seçim Yöntemi ve Yapay Bağışıklık Sistemi

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