Abstract:
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+02, 0.18%, ≥ 0.99 and ≥ 99, respectively.
