Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

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.

Lie Recognition with Multi-Modal Spatial–Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory

“…Furthermore, hand features are very stable cues for identifying human actions and intentions, as reported in the literature [ 3 , 18 , 19 ]. Lu et al [ 20 ] extracted hand and facial features using color 3-D LUT, which are further utilized with blob analysis to track head and hand motions (behavioral state).…”

“…We obtained a huge number of features from the fully connected layer but features with high confidence scores can improve the computational speed of ResNet-152-BLSTM learning and avoid ignoring important variances [ 3 ]. The confidence score of all features is illustrated in Table 4 .…”

“…The scenes from the top left corner to the right show the behavioral attitudes of lying people, while the scenes from the bottom left corner to the right show the behavioral attitudes of truth-telling people. Addressing the problem of learning human involuntary cues, recent research in the field of image processing/CV and machine learning reshapes computer engineering systems into machine learning-based (ML) systems [ 2 , 3 , 4 ]. ML-based systems can learn tiny facial marks [ 4 ] and behaviors in connection with body motion, as well as hand gestures [ 5 ], therefore making lie recognition suitable via CV and ML techniques.…”

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition

“…The literature offers several methodologies (Mishra et al, 2021) that could be used for spectral reconstruction. For instance, O2-PLS (Trygg and Wold, 2003) and OnPLS (Lofstedt et al, 2013) utilise spectral data's local and global joints, bioheat models (Alzahrani and Abbas, 2019;Marin et al, 2021) could be adapted to predict the internal tomato tissues and adaptive neuro-fuzzy inference system (Abdullahi et al, 2021;Abdullahi et al, 2022), a hybrid computational model that combines the adaptive capabilities of neural networks with the interpretability of a mathematical framework that deals with uncertainty and imprecision in decision-making. However, these methods are not hierarchical and do not allow for convolution and fusion of information in a superset or deconvolution in a reverse way.…”

Application of Intelligent Lie Recognition Technology in Laws and Regulations Based on Occupational Mental Health Protection

“…However, with the increase of sub-locks, the spatial dimension of features also increased, resulting in the problem of information redundancy. 8 …”