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Register a new userThe Reliability and Acceptance of Biometric System in Bangladesh: Users Perspective
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Added by
Shaykh Siddique
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Biometric systems are the latest technologies of unique identification. People all over the world prefer to use this unique identification technology for their authentication security. The goal of this research is to evaluate the biometric systems based on system reliability and user satisfaction. As technology fully depends on personal data, so in terms of the quality and reliability of biometric systems, user satisfaction is a principal factor. To walk with the digital era, it is extremely important to assess users concerns about data security as the systems are conducted the authentication by analyzing users personal data. The study shows that users are satisfied by using biometric systems rather than other security systems. Besides, hardware failure is a big issue faced by biometric systems users. Finally, a matrix is generated to compare the performance of popular biometric systems from the users opinions. As system reliability and user satisfaction are the focused issue of this research, biometric service providers can use these phenomena to find what aspect of improvement they need for their services. Also, this study can be a great visualizer for Bangladeshi users, so that they can easily realize which biometric system they have to choose.
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With the rapid advancement of technology, different biometric user authentication, and identification systems are emerging. Traditional biometric systems like face, fingerprint, and iris recognition, keystroke dynamics, etc. are prone to cyber-attacks and suffer from different disadvantages. Electroencephalography (EEG) based authentication has shown promise in overcoming these limitations. However, EEG-based authentication is less accurate due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based identification offers high accuracy but suffers from different spoofing attacks. To overcome these challenges, we propose a novel multimodal biometric system combining EEG and keystroke dynamics. Firstly, a dataset was created by acquiring both keystroke dynamics and EEG signals from 10 users with 500 trials per user at 10 different sessions. Different statistical, time, and frequency domain features were extracted and ranked from the EEG signals and key features were extracted from the keystroke dynamics. Different classifiers were trained, validated, and tested for both individual and combined modalities for two different classification strategies - personalized and generalized. Results show that very high accuracy can be achieved both in generalized and personalized cases for the combination of EEG and keystroke dynamics. The identification and authentication accuracies were found to be 99.80% and 99.68% for Extreme Gradient Boosting (XGBoost) and Random Forest classifiers, respectively which outperform the individual modalities with a significant margin (around 5 percent). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method is secured and reliable for any kind of biometric authentication.
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