Multimodal Biometric Authentication with Secured Templates — A Review

Choudhary, Swati K.; Naik, Ameya K.

doi:10.1109/icoei.2019.8862563

Cited by 15 publications

(8 citation statements)

References 31 publications

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“…Kodituwakku [ 140 ] believes biometric technology can be classified into two general categories: physiological biometric techniques and behavioral biometric techniques. Jain et al [ 141 ] and Choudhary and Naik [ 142 ] also classify biometrics into two categories: physiological and behavioral. In the literature, not only signature, voice, and gait are considered behavioral biometric features, but also ECG, EMG, and EEG [ 143 ], while other authors distinguish cognitive biometrics [ 144 , 145 ], including electroencephalography (EEG), electrocardiography (ECG), electrodermal response (EDR), blood pulse volume (BVP), near-infrared spectroscopy (NIR), electromyography (EMG), eye trackers (pupillometry), hemoencephalography (HEG), and related technologies [ 145 ].…”

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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“…In light of growing privacy concerns with regard to data usage, countries enacted laws to protect personal data by imposing solutions to mitigate the problem of attacks and data leaks [2], [3]. For many years, the main approach adopted to ensure biometric networks' security and privacy was applying biometric template techniques [1], [4]- [8]. The work of Ross et al [9] pushes a lot of achievements to deal with template protection using biometric cryptosystems [10]- [17] and cancelable biometric (feature transformations) [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

Improving Data Security, Privacy, and Interoperability for the IEEE Biometric Open Protocol Standard

Filho

Sousa

et al. 2022

IEEE Access

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“…Privacy is contextually based on the specifics of who should do what and where [ 1 ]. Recently, the availability of low-cost data that aids in ample data communication, securing data and applications from unauthorized identities is becoming very important to help control access to software, data, devices and physical buildings [ 2 ]. Despite the widespread use of passwords to authenticate users, there are many weaknesses and drawbacks associated with this method of user authentication [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Object Selection as a Biometric

Tlhoolebe

Dai

2022

Entropy

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The use of eye movement as a biometric is a new biometric technology that is now in competition with many other technologies such as the fingerprint, face recognition, ear recognition and many others. Problems encountered with these authentication methods such as passwords and tokens have led to the emergence of biometric authentication techniques. Biometric authentication involves the use of physical or behavioral characteristics to identify people. In biometric authentication, feature extraction is a very vital stage, although some of the extracted features that are not very useful may lead to the degradation of the biometric system performance. Object selection using eye movement as a technique for biometric authentication was proposed for this study. To achieve this, an experiment for collecting eye movement data for biometric purposes was conducted. Eye movement data were measured from twenty participants during choosing and finding of still objects. The eye-tracking equipment used was able to measure eye-movement data. The model proposed in this paper aimed to create a template from these observations that tried to assign a unique binary signature for each enrolled user. Error correction is used in authenticating a user who submits an eye movement sample for enrollment. The XORed Biometric template is further secured by multiplication with an identity matrix of size (n × n). These results show positive feedback on this model as individuals can be uniquely identified by their eye movement features. The use of hamming distance as additional verification helper increased model performance significantly. The proposed scheme has a 37% FRR and a 27% FAR based on the 400 trials, which are very promising results for future improvements.

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Multimodal Biometric Authentication with Secured Templates — A Review

Cited by 15 publications

References 31 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

Improving Data Security, Privacy, and Interoperability for the IEEE Biometric Open Protocol Standard

Object Selection as a Biometric

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