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Abstract
Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique.
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Abstract
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.
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