Chapters authored
Image Sharpness-Based System Design for Touchless Palmprint Recognition
Currently, many palmprint acquisition devices have been proposed, but how to design the systems are seldom studied, such as how to choose the imaging sensor, the lens, and the working distance. This chapter aims to find the relationship between image sharpness and recognition performance and then utilize this information to direct the system design. In this chapter, firstly, we introduce the development of recent palmprint acquisition systems and abstract their basic frameworks to propose the key problems needed to be solved when designing new systems. Secondly, the relationship between the palm distance in the field of view (FOV) and image pixels per inch (PPI) is studied based on the imaging model. Suggestions about how to select the imaging sensor and camera lens are provided. Thirdly, image blur and depth of focus (DOF) are taken into consideration; the recognition performances of the image layers in the Gaussian scale space are analyzed. Based on this, an image sharpness range is determined for optimal imaging. The experiment results are obtained using different algorithms on various touchless palmprint databases collected using different kinds of devices. They could be references for new system design.
Part of the book: Biometric Systems
Region of Interest Localization Methods for Publicly Available Palmprint Databases
So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research.
Part of the book: Biometric Systems