Fig. 1. Flow chart of proposed method

Fig. 2. Schematic of palm vein feature extraction

Fig. 3. Flow chart of random forest training

Fig. 4. Acquisition device and five examples of different human palm vein images collected using this device. (a) PolyU database; (b) CASIA database; (c) self-built database

Fig. 5. Misclassified images and pseudo color images. (a) Class-1 example of misclassified image; (b) class-2 example of misclassified image; (c) class-3 example of misclassified images; (d) class-4 example of misclassified images; (e) class-5 example of misclassified images; (f) class-6 example of misclassified images; (g) class-7 example of misclassified images; (h) class-8 example of misclassified images; (i) class-9 example of misclassified images; (j) class-10 example of misclassified images; (k) c

Fig. 6. Palm vein ROI map, feature map and pseudo color image. (a) ROI map; (b) pseudo color image of Fig. (a); (c) feature map extracted by method in Ref. [11]; (d) pseudo color image of Fig. (c); (e) feature map extracted from 4th convolutional layer; (f) pseudo color image of Fig. (e)

Fig. 7. Classification error of each database versus number of classification decision trees

Table 1. Classification errors of palm vein features extracted from different layers of AlexNet network on different databases%

Table 2. Effect of PCA on classification error of each database%

Table 3. Misclassification data classification errors from different methods

Table 4. Recognition accuracy of each method%