In:
電腦學刊, Angle Publishing Co., Ltd., Vol. 34, No. 1 ( 2023-02), p. 013-027
Abstract:
〈p〉To improve pattern representation capabilities and robustness in traditional finger-vein recognition algorithms. In this paper, we propose FFV-MBC, a novel fused finger-vein recognition method based on monogenic binary coding (MBC). First of all, the amplitude, orientation, and phase information of the finger-vein images are filtered by a multi-scale monogenic log-Gabor filter and encoded by the binary coding theory. Three local features, MBC-A, MBC-P, and MBC-O, are achieved from different combinations of local image intensity and variation coding. After obtaining the features, we utilize the block-based Fisher Linear Discriminant method to reduce the dimension. Finally, the similarity components are calculated by the cosine distance and fused for the final finger-vein recognition results. We evaluate our proposed method on two publicly available datasets and one self-built dataset, i.e., Malaysian Polytechnic University (FV-USM), the Group of Machine Learning and Applications of Shandong University (SDUMLA-HMT), and our team, Signal and Information Processing Laboratory (FV-SIPL). On average, the proposed method achieved high recognition accuracy, i.e., 99.30%, and 1.10% equal error rates (EER). Overall, the proposed method performs better than most classical and state-of-the-art finger-vein recognition methods.〈/p〉
〈p〉 〈/p〉
Type of Medium:
Online Resource
ISSN:
1991-1599
,
1991-1599
Uniform Title:
FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding
DOI:
10.53106/199115992023023401002
Language:
Unknown
Publisher:
Angle Publishing Co., Ltd.
Publication Date:
2023
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