Electrophysiological Biometrics: Opportunities and Risks

Riera, Alejandro; Dunne, Stephen; Cester, Ivan; Ruffini, Giulio

doi:10.1007/978-94-007-3892-8_7

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…The primary purpose of first-generation biometrics is identity verification, such as facial recognition, and the technology is built around simple sensors that capture physical features and store them for later use [ 153 ]. Second-generation biometrics can also be used to detect emotions, with electro-physiologic and behavioral biometrics (e.g., based on ECG, EEG, and EMG) as examples of such technologies [ 154 ]. Second-generation biometrics measure individual patterns of learned behavior or physiological processes, rather than physical traits, and are also known as behavioral biometrics [ 155 ].…”

Section: Brain and Biometric Affect Sensorsmentioning

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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Section: Brain and Biometric Affect Sensorsmentioning

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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show abstract

“…Recent work has shown that electrophysiological signals-dependant on the electrical properties of tissues and cells-are distinctive enough for each individual person to be used for biometric applications, with the additional bonus of being inherently difficult, though not impossible, to forge. Bioelectrical signals recorded from the heart (electrocardiography, ECG) and the brain (electroencephalography, EEG) seem particularly well suited for these purposes [2].…”

mentioning

confidence: 99%

Biometrics' new identity—measuring more physical and biological traits

Rinaldi¹

2015

EMBO Reports

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Single Trial Authentication with Mental Password Writing

Abdulkader

Atia

Mostafa

2015

Lecture Notes in Computer Science

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Abstract. This paper presents an authentication system that uses brain waves as a biometric discriminant trait. It utilizes Electroencephalogram (EEG) signals generated from mental writing of the user-owned password. Independent Component Analysis (ICA) and baseline correction has been used for preprocessing and noise removal. The effect of two types of features, multivariate autoregressive (MVAR) model parameters and power spectral density (PSD) features, have been studied for this activity. Performance results based on single trial analysis have revealed that imagined password writing can reach average Half Total Error Rate (HTER) of 5 % for PSD features vs 3 % obtained with MVAR coefficients. The experiments have shown that mental password writing can be used for increasing the user acceptance for enrollment conditions while maintaining high performance results.

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Electrophysiological Biometrics: Opportunities and Risks

Cited by 5 publications

References 34 publications

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

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

Biometrics' new identity—measuring more physical and biological traits

Single Trial Authentication with Mental Password Writing

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