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About Advanced Palmprint Authentication
This book presents a comprehensive exploration of palmprint recognition, synthesizing over a decade of research in contact-based, contactless, 3D, and multispectral systems. As one of the earliest approaches in biometrics, contact-based palmprint systems have evolved significantly, achieving greater portability and accuracy, even when handling large-scale datasets. In contrast, contactless systems, which allow users to position their palms near the camera without physical contact, offer a hygienic, user-friendly alternative that has quickly gained popularity in various applications. Additionally, the advancement of 3D palmprint recognition and the introduction of cutting-edge sensors, such as line-scan and multicamera systems, have further enhanced the accuracy and reliability of these systems. This book is structured into 13 chapters, divided into three key sections. The first part delves into contact-based systems, emphasizing their growing efficiency and performance in both small devices and large-scale scenarios. The second part provides in-depth coverage of contactless systems, detailing essential processes like palmprint acquisition, ROI localization, feature extraction, and matching techniques. The third section examines the latest developments in multiple sensing systems, focusing on 3D and multispectral recognition. With the significant achievement of Deep Learning in computer vision, many methods in palmprint recognition applied the CNN as a key component for feature extraction. Here, the coding-based methods were implemented by Python using the NumPy library.
Detailed Information
| Author: | David Zhang |
|---|---|
| Pages: | 322 |
| Language: | English |
| File Size: | 25 |
| Format: | |
| Price: | FREE |
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