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您的检索式：作者名="DavidZhang"
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| 1 | Wavelet Energy Feature Extraction and Matching for Palmprint Recognition显示文摘According to the fact that the basic features of a palmprint, including principal lines, wrinkles and ridges,have different resolutions, in this paper we analyze palmprints using a multi-resolution method and define a novel palmprint feature, which called wavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in different directions at different resolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses the discriminabilities of each level WEF and, according to these discriminabilities, chooses a suitable weight for each level to compute the weighted city block distance for recognition. The experimental results show that the order of the discriminabilities of each level WEF, from strong to weak, is the 4th, 3rd,5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation and translation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprint verification and palmprint identification, respectively. These results demonstrate the power of the proposed approach. | Xiang-QianWu Kuan-QuanWang DavidZhang | 2005 | Journal of Computer Science & Technology2005，20，3： | 19 |
| 2 | Online Palmprint Identification System for Civil Applications显示文摘In this paper, a novel biontetric identification system is presented to identify a person's identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets at the civil applications, which require identifying a person in a large database with high accuracy in real-time. The system is constituted by four major components: User Interface Module, Acquisition Module, Recognition Module and External Module. More than 7,000 palmprint images have been collected to test the performance of the system. The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuine acceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017% and a false rejection rate, 0.86%. The execution time for the whole process including image collection, preprocessing, feature extraction and matching is less than 1 second. | DavidZhang Guang-MingLu AdamsWai-KinKong MichaelWong | 2005 | Journal of Computer Science & Technology2005，20，1： | 4 |
| 3 | Rotation invariant texture classification using LBP variance (LBPV) with global matching显示文摘 | ZHENHUA GUO LEIZHANG DAVIDZHANG | | 0，，： | 1 |
| 4 | Two - stage image denoising by principalcomponent analysis with lo- cal pixel grouping 显示文摘 | Lei Zhang Weisheng Dong DavidZhang | 2010 | Pattern Recognition2010，43，： | 1 |
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