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نتیجه جستجو - Internet of biometric things

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Zero shot augmentation learning in internet of biometric things for health signal processing
یادگیری تقویتی صفر در اینترنت اشیا بیومتریک برای پردازش سیگنال سلامتی-2021
In recent years, the number of Internet of Things (IoT) devices has increased rapidly. The Internet of Biometric Things (IoBT) can process biometrics and health signals, and it will greatly extend the range of biometric applications. The analysis of health signals in the IoBT can use computer-aided diagnosis techniques. However, most of the existing computer-aided diagnosis methods are developed for common diseases and are not suitable for rare diseases. Zero shot learning is a potential method for the computer- aided diagnosis of rare diseases because it can identify objects of unknown categories. However, the ex- isting zero shot learning methods are based on attribute learning and rely on an attribute dataset. There is no attribute dataset for health signal processing. Therefore, the existing zero shot learning methods are not suitable for health signal processing. Based on the above background, we propose a zero shot aug- mentation learning model (ZSAL) in the IoBT for health signal processing. First, an expert doctor identifies the contour of a lesion and selects a background image without a lesion. Second, the computer automatically generates virtual images using zero shot augmentation technology. Finally, the generated virtual dataset is used to train a convolutional classifier, and then we apply the classifier to the computer-aided diagnosis of actual medical images. The experiment shows the efficiency and effectiveness of our method.© 2021 Elsevier B.V. All rights reserved.
Keywords: Internet of biometric things | Zero shot learning | Data augmentation | Health signal processing
مقاله انگلیسی
2 Are IoBT services accessible to everyone?
آیا خدمات IoBT برای همه قابل دسترسی است؟-2021
Biometric recognition aims at identifying a person by using their physiological or behavioral characteristics. When adopted for improving the security in the Internet of Things (IoT) field, it is commonly named Internet of Biometric Things (IoBT). However, despite its advantages there are further considerations on security and different ethical and legal issues, such as the possibility of exclusion of individuals due to pathologies, injuries, disabilities, or genetic defects. Indeed, these specific physical condition would lead to not satisfy the requirements commonly used for biometric recognition. As a consequence, the limitations of current biometric systems can exclude a person from the use of IoBT services. In this paper, we focus on the difficulty of iris recognition when it is affected by Coloboma, a congenital abnormality of membranes of the eye. We show how this pathological state impacts on the performance of the Daugman and Canny edge detection algorithms, which represent the most widespread methods used for the iris localization step in eye-based biometric. Results of an experimentation revealed that they correctly detected only 15.79% and 47.37% of Coloboma iris, respectively. In order to avoid the use of these inaccurate algorithms in case of Coloboma eye, we designed and experimented a Residual Neural Network classifier able to detect the presence of this disease with 99.79% of accuracy. This classifier may be a first step towards a more sophisticated “diversity-aware” biometric system which represents an alternative to actual IoBT authentication method for people with special physical condition.
Keywords: Security and IoBT | Iris recognition | Deep learning for IoBT applications
مقاله انگلیسی
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