Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach. (September 2017)

• Record Type: Journal Article
• Authors: Zhang, Lin; Li, Lida; Yang, Anqi; Shen, Ying; Yang, Meng
• Is Part Of: Pattern recognition. Volume 69(2017:Sep.)
• Page Start: 199
• Page End: 212
• Publication Date: 2017-09
• Subjects: Contactless palmprint recognition -- Collaborative representation -- Block-wise features -- Image quality assessment; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.04.016
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel device is designed and developed for capturing contactless palmprint images. A large-scale contactless palmprint image dataset is established. The quality of collected images is analyzed using modern image quality assessment metrics. For contactless palmprint identification, a CR-based approach is proposed, which is highly effective and efficient. Abstract: Biometric authentication has been found to be an effective method for recognizing a person's identity with a high confidence. In this field, the use of palmprint represents a recent trend. To make the palmprint-based recognition systems more user-friendly and sanitary, researchers have been investigating how to design such systems in a contactless manner. Though substantial effort has been devoted to this area, it is still not quite clear about the discriminant power of the contactless palmprint, mainly owing to lack of a public, large-scale, and high-quality benchmark dataset collected using a well-designed device. As an attempt to fill this gap, we have at first developed a highly user-friendly device for capturing high-quality contactless palmprint images. Then, with the developed device, a large-scale palmprint image dataset is established, comprising 12, 000 images collected from 600 different palms in two separate sessions. To the best of our knowledge, it is the largest contactless palmprint image benchmark dataset ever collected. Besides, for the first time, the quality of collected images is analyzed using modern image quality assessment metrics. Furthermore, for contactless palmprint identification, we have proposed a novel approach, namely CR_CompCode, which can achieve high recognition accuracy while having an extremely low computational complexity. To make the results fully reproducible, the collected dataset and the related source codes are publicly available athttp://sse.tongji.edu.cn/linzhang/contactlesspalm/index.htm .