According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a ...According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a novel palmprint feature, which calledwavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energydistribution of the principal lines, wrinkles and ridges in different directions at differentresolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses thediscriminabilities of each level WEF and, according to these discriminabilities, chooses a suitableweight for each level to compute the weighted city block distance for recognition. The experimentalresults show that the order of the discriminabilities of each level WEF, from strong to weak, is the4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation andtranslation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprintverification and palmprint identification, respectively. These results demonstrate the power of theproposed approach.展开更多
In this paper, a novel biometric identification system is presented toidentify a person''s identity by his/her palmprint. In contrast to existing palmprint systems forcriminal applications, the proposed system...In this paper, a novel biometric identification system is presented toidentify a person''s identity by his/her palmprint. In contrast to existing palmprint systems forcriminal applications, the proposed system targets at the civil applications, which requireidentifying a person in a large database with high accuracy in real-time. The system is constitutedby four major components: User Interface Module, Acquisition Module, Recognition Module and ExternalModule. 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 genuineacceptance 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 imagecollection, preprocessing, feature extraction and matching is less than 1 second.展开更多
文摘According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a novel palmprint feature, which calledwavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energydistribution of the principal lines, wrinkles and ridges in different directions at differentresolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses thediscriminabilities of each level WEF and, according to these discriminabilities, chooses a suitableweight for each level to compute the weighted city block distance for recognition. The experimentalresults show that the order of the discriminabilities of each level WEF, from strong to weak, is the4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation andtranslation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprintverification and palmprint identification, respectively. These results demonstrate the power of theproposed approach.
文摘In this paper, a novel biometric identification system is presented toidentify a person''s identity by his/her palmprint. In contrast to existing palmprint systems forcriminal applications, the proposed system targets at the civil applications, which requireidentifying a person in a large database with high accuracy in real-time. The system is constitutedby four major components: User Interface Module, Acquisition Module, Recognition Module and ExternalModule. 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 genuineacceptance 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 imagecollection, preprocessing, feature extraction and matching is less than 1 second.
