Xue-bin XU, Xiao-min XING, Mei-juan AN, Shu-xin CAO, Kan MENG, Long-bin LU. Palmprint Recognition Method Based on Multispectral Image Fusion[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3615
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 11, 3615 (2022)
Fig. 1. Schemetic diagram of the multispectral palmprint imaging device
Fig. 2. Multispectral palmprint ROI extraction process
(a): Near-infrared palmprint; (b): Binarization; (c): Maximum incision circle; (d): Near-infrared palmprint ROI
(a): Near-infrared palmprint; (b): Binarization; (c): Maximum incision circle; (d): Near-infrared palmprint ROI
Fig. 3. Multispectral palmprint ROI extraction process
Fig. 4. Mapping of local maxima and local minima
(a): Source signal; (b): Local maxima map; (c): Local minima map
(a): Source signal; (b): Local maxima map; (c): Local minima map
Fig. 5. Decompositions of a palmprint image using FABEMD
(a): Green ROI; (b): BIMF1; (c): BIMF2; (d): BIMF3; (e): BIMF4; (f): The residue; (g): The sum of K BIMF
(a): Green ROI; (b): BIMF1; (c): BIMF2; (d): BIMF3; (e): BIMF4; (f): The residue; (g): The sum of K BIMF
Fig. 6. Near-infrared palmprint image processing
(a): NIR ROI; (b):SI ; (c): ${{\tilde{S}}_{I}}$(Bezier interpolation); (d): S'I (feature refinement)
(a): NIR ROI; (b):
Fig. 7. Multispectral palmprint fusion image
(a): Fused images without feature compression;(b): Fused images with feature compression
(a): Fused images without feature compression;(b): Fused images with feature compression
Fig. 8. Multispectral palmprinu fusion process
(a):S R (red in FABEMD); (b): S G; (c): S B; (d): S″I (NIR with feature compression); (e): Fusion process
(a):
Fig. 9. Network architecture of improved residual channel attention network (IRCANet)
Fig. 10. Original ResBlock and ResStageBlock
Fig. 11. Improved residual block with channel attention
Fig. 12. Multispectral palmprint samples in the PolyU database
Fig. 13. Accuracies of different K values in FABEMD
Fig. 14. Accuracies of different fusion strategies for multispectral (R, G, B, N) palmprint images
Fig. 15. Optimal results of different residual structures for multispectral (R, G, B, N) palmprint images
Fig. 16. Recognition effect of different mainstream networks for multispectral palmprint fusion images
(a): Recognition trends; (b): Accuracy contrast
(a): Recognition trends; (b): Accuracy contrast
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Table 1. Average accuracies of different IRCANet parameters
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Table 2. Accuracies of multispectral combinations under different fusion strategies
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Table 3. Accuracies of multispectral combinations under different residual structures
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Table 4. Comparison of multispectral palmprint recognition algorithms
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Table 5. Time cost of the proposed method
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