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Finger knuckle biometric feature selection based on the FIS_DE optimization algorithm

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Abstract

In recent years, the hand-based biometric system has received significant attention in identifying a person. In the hand-based biometric, the finger knuckle print plays a vital role in recognizing a person. The main purpose of this research is to propose an effective feature optimization technique for identifying the best feature vectors for finger knuckle print-based authentication. This work presents a novel feature selection algorithm, fitness index selection with differential evolution (FIS_DE), based on K-nearest neighbor (KNN). Initially, the feature extraction is performed using the conventional methods like principal component analysis, linear discriminant analysis, and independent component analysis. Then, evolutionary algorithm is used for feature selection with best vectors. The population-based metaheuristic algorithm DE proposed is used to optimize the KNN classifier. FIS_DE_KNN is compared with Euclidean and neural network classifiers to show the improved efficiency of the proposed work. In this research, experimental results of the proposed FIS_DE-KNN improve the classification accuracy with an optimized number of features.

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Abbreviations

FKP:

Finger knuckle print

BL_POC:

Band-limited phase-only correlation

GA:

Genetic algorithm

FS:

Feature selection

FSGS:

Feature selection gaining-sharing knowledge-based

EGA:

Exponential genetic algorithm

SCMO:

Speed-constrained multi-objective

PSO:

Particle swarm optimization

FRR:

False rejection rate

FAR:

False acceptance rate

PCA:

Principal component analysis

LDA:

Linear discriminant analysis

KNN:

K-nearest neighbor

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Authors and Affiliations

  1. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    P. Jayapriya & K. Umamaheswari

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  1. P. Jayapriya
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  2. K. Umamaheswari
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Correspondence to P. Jayapriya.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and company.

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We are not used any animals and human for our research. The manuscript not submitted anywhere for publications and all work mentioned in this paper is authors own work. We did not use any third-party software for surveys, scales, and studies.

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Jayapriya, P., Umamaheswari, K. Finger knuckle biometric feature selection based on the FIS_DE optimization algorithm. Neural Comput & Applic 34, 5535–5547 (2022). https://doi.org/10.1007/s00521-021-06705-0

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