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

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

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

Fig. 6. Near-infrared palmprint image processing
(a): NIR ROI; (b): S_I; (c): ${{\tilde{S}}_{I}}$(Bezier interpolation); (d): S'_I(feature refinement)

Fig. 7. Multispectral palmprint fusion image
(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

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