Using Behavioral Biometrics of Fingerprint Authentication to Investigate Physical and Emotional User States

Salman, Adan S.; Salman, Ammar S.; Salman, Odai S.

doi:10.1007/978-3-030-89880-9_19

Cited by 2 publications

(1 citation statement)

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“…Khushaba et al [ 25 ] used brain and biometric sensors to integrate EEG and eye tracking for assessing the brain response. Other scientists [ 28 , 29 , 30 , 31 , 32 , 33 ] used the following biometric sensors in their studies: heart rate, pulse rate variability, odor, pupil dilation and contraction, skin temperature, face recognition, voice, signature, gestures, and others.…”

Section: Introductionmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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