An efficient feature selection scheme based on genetic algorithm for ear biometrics authentication

Ghoualmi, Lamis; Draa, Amer; Chikhi, Salim

doi:10.1109/isps.2015.7244991

Cited by 21 publications

(16 citation statements)

References 12 publications

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“…Feature selection in various biometrics such as the hand based, palm print, ear and face [10,12,14,40,41] are compared to the proposed feature selection method MMBOA_mr and GWO. The results are tabulated in Tab.…”

Section: Comparison Of the Proposed Mmboa_mr With Other Metaheuristic Algorithmsmentioning

confidence: 99%

“…As per literature, only a few optimization techniques are used in biometric recognition for feature selection such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Optimization techniques are mostly used in iris, ear, palm print and in multimodal biometric recognition [10][11][12][13][14][15]. Even the number of algorithms related to bio-inspired and evolutionary algorithms is used in solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition

Jayapriya¹,

Umamaheswari²

2022

Intelligent Automation &Amp; Soft Computing

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Section: Comparison Of the Proposed Mmboa_mr With Other Metaheuristic Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition

Jayapriya¹,

Umamaheswari²

2022

Intelligent Automation &Amp; Soft Computing

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“…Ear curves are extracted for an ear recognition system in [14]. The face recognition system in [15] has used the techniques like wavelet transform, spatial differentiation and twin pose testing scheme for feature extraction from faces. According to [Ukpai et al, 2015], principal texture pattern and dual tree complex wavelet transform produce iris-specific features from an iris image.…”

Section: Feature Extractionmentioning

confidence: 99%

Machine Learning Biometric Attendance System using Fingerprint Fuzzy Vault Scheme Algorithm and Multi-Task Convolution Neural Network Face Recognition Algorithm

Cerna¹,

Abas²,

Gebreziagbher³

et al. 2018

IJCA

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In many higher education institutions in particular in Africa including Ethiopia proper attendance monitoring is a very important criteria in providing quality to promote student satisfaction. Biometric technology offers an advanced verification for employees used in most schools and companies. A prototype using biometric technology has been proposed to facilitate the recording of the employees' attendance and generate automatically the payroll The prototype utilize Adafruit Fingerprint Fuzzy Vault scheme algorithm through Raspberry Pi 3, Pi Camera for face detection and recognition using Multi-task Convolution Neural Network (MTCNN) Method and output simulated through MATLAB respectively. A user interface module was develop using Visual Basic where fingerprint and face registration is done and during logging with the prototype. The resulting prototype was tested in the non-academic staff of Federal TVET Institute, an institute of higher learning specializing Technical Vocational Education and Training (TVET) offering both undergraduate and postgraduate program in Addis Ababa, Ethiopia.

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“…In light of this, bio-inspired artificial intelligence techniques have been proposed. The Genetic Algorithm (GA) [19] and Particle Swarm Optimisation (PSO) [15,16] have given promising results in feature selection task, which encouraged us to apply a recent powerful metaheuristic for the proposed feature selection scheme.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

Ghoualmi

Benkechakche

Draa

2022

Preprint

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Recently, both industry and academics are paying increasingly more attention to bio-metric authentication using palm vein patterns due to the benefits it provides over traditional methods, like those that rely on a fingerprint, iris, or face. However, feature extraction of the palm vein feature, which seeks to collect the most distinctive features that uniquely identify individuals might have redundant and useless features, which would reduce the performance of the bio-metric system and cause a dimension issue. This paper presents an improved feature selection technique based on a bio-inspired artificial intelligence algorithm called Discrete Artificial Bee Colony (DABC) for palm vein bio-metric authen-tication. The best set of features from those offered by Gabor feature extractor are selected using the DABC algorithm. In attempt to grant additional diversity and effectively explore the solution space, a variant of Opposition Based Learning (OBL) notion is implemented. The proposed approach, termed Modified Opposition based DABC (MODABC), has been evaluated using the CASIA palm vein database in comparison to a number of feature selection algorithms based on bio-inspired artificial intelligence. The experimental findings demonstrate that the presented method perform better that the literature in a number of areas, including accuracy, FRR, FAR, feature vector size, and fitness values. The method being proposed enables providing a suitable balance between the feature vector size and the authentication rate.

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An efficient feature selection scheme based on genetic algorithm for ear biometrics authentication

Cited by 21 publications

References 12 publications

Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition

Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition

Machine Learning Biometric Attendance System using Fingerprint Fuzzy Vault Scheme Algorithm and Multi-Task Convolution Neural Network Face Recognition Algorithm

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

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