Person Identification based on Physiological Signals: Conditions and Risks

Bellmann, Peter; Thiam, Patrick; Schwenker, Friedhelm

doi:10.5220/0008865503730380

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, False to acquire (FTA) is the percentage of attempts in which the system fails to acquire a sample of adequate quality. FNMR can be computed using (6).…”

Section: Biometric Quality Metric Of Effectiveness Evaluation Criteriamentioning

confidence: 99%

“…Moreover, IoT technology can improve the accuracy and efficiency [4] of biometric recognition systems by enabling the integration of several biometric modalities, such as facial recognition and fingerprint identification [5]. These systems can leverage physiological or behavioral characteristics to identify individuals uniquely, lower the chance of cheating, and enhance educational security [6]. IoT-based biometric recognition systems can provide a more secure and convenient method of identifying and confirming students during classes and exams in the educational setting.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IoT-Based Biometric Recognition Systems in Education for Identity Verification Services: Quality Assessment Approach

2023

View full text Add to dashboard Cite

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards are outdated personal information. Several biometric recognition approaches have been proposed and can be adopted to strengthen students' identity verification. Most educational adoption technology struggles with evaluation and validation techniques to ensure that biometric recognition systems are unquestionably suitable for utilization and implementation to fulfill student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. A unimodal face biometric system promises excellent support. A unimodal fingerprint biometric system assures the second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with few handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational use.

show abstract

“…In addition, False to acquire (FTA) is the percentage of attempts in which the system fails to acquire a sample of adequate quality. FNMR can be computed using (6).…”

Section: Biometric Quality Metric Of Effectiveness Evaluation Criteriamentioning

confidence: 99%