篇名 |
FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding
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並列篇名 | FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding |
作者 | Zhi-Yong Tao、Meng Wang、Xin-Ru Zhou、Jie Li、Sen Lin |
英文摘要 | 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.
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起訖頁 | 013-027 |
關鍵詞 | finger-vein recognition、monogenic binary coding、multi-scale monogenic log-Gabor filter、weighted fused |
刊名 | 電腦學刊 |
期數 | 202302 (34:1期) |
DOI |
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