Название: Machine Learning for Biometrics: Concepts, Algorithms and ApplicationsАвтор: Partha Pratim Sarangi, Madhumita Panda, Subhashree Mishra
Издательство: Academic Press/Elsevier
Год: 2022
Страниц: 240
Язык: английский
Формат: pdf (true)
Размер: 17.8 MB
Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc. In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.
The biometric systems automatically recognize the identity of a person by using his/her physiological and behavioral characteristics. The improvement in sensor technology attracts many researchers’ attention to introduce a number of new biometric traits for providing privacy and security in various applications ranging from industrial to healthcare systems. In the last decades, with the aim of improving the recognition performance, a large number of Machine Learning algorithms have been presented in numerous biometric applications. The significance of machine learning algorithm is to determine the identity of individuals by associating biometric patterns with their respective enrolled users. However, in high-security applications, security and recognition accuracy are major concerns that can be addressed by designing multimodal biometric systems. As the name depicts, multimodal biometrics incorporates two or more different biometric characteristics (for example, fingerprint, iris, face, plamprint, etc.), which works in the similar way like unimodal biometrics except an extra module of information fusion. Due to the increasing number of biometric systems and their applications, a profound amount of research and developments are still required in this field of biometrics.
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