Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

ABDULLAHI, S. U. N. U. S. I. B. A. L. A.

doi:10.1109/ACCESS.2022.3171850

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

2022-01-01

12

OA

AI

A

ABDULLAHI, S. U. N. U. S. I. B. A. L. A.

C

Chainarong Khunpanuk *

Z

Zakariyya Abdullahi Bature

ABUBAKAR, A. U. W. A. L. B. A. L. A.

I

IBRAHIM, A. B. D. U. L. K. A. R. I. M. H. A. S. S. A. N.

DOI：10.1109/ACCESS.2022.3171850

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Abstract

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Addressing crime detection, cyber security and multi-modal gaze estimation in biometric information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms such as Support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) have been proposed to recognize distinct and discriminant features of biometric information (intrinsic hand features and demographic cues) with good classification accuracy. Unfortunately, due to nonlinearity in distinct and discriminant features of biometric information, accuracy of SVM and ANFIS is reduced. As a result, optimized AI algorithms ((ANFIS) with subtractive clustering (ANFIS-SC) and SVM with error correction output code (SVM-ECOC)) have shown to be effective for biometric information recognition. In this paper, we compare the performance of the ANFIS-SC and SVM-ECOC algorithms in their effectiveness at learning essential characteristics of intrinsic hand features and demographic cues based on Pearson correlation coefficient (PCC) feature selection. Furthermore, the accuracy of these algorithms are presented, and their recognition performances are evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), scatter index (SI), mean absolute deviation (MAD), coefficient of determination (R-2), Akaike's Information Criterion (AICc) and Nash-Sutcliffe model efficiency index (NSE). Evaluation results show that both SVM-ECOC and ANFIS-SC algorithms are suitable for accurately recognizing soft biometric information on basis of intrinsic hand measurements and demographic cues. Moreover, comparison results demonstrated that ANFIS-SC algorithms can provide better recognition accuracy, with RMSE, AICc, MAPE, R-2 and NSE values of <= 3.85, 2.39E C02, 0.18%, >= 0.99 and >= 99, respectively.

Keywords:

ANFIS

artificial intelligence

biometric features recognition

demographic cues

SVM

misclassifications

optimization

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

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