2022
DOI: 10.1109/access.2022.3171850
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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… Show more

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Cited by 10 publications
(3 citation statements)
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“…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).…”
Section: Literature Reviewmentioning
confidence: 91%
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“…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).…”
Section: Literature Reviewmentioning
confidence: 91%
“…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 .…”
Section: Methodsmentioning
confidence: 99%
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