# Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance
## 3 Experiments
### Experimental Results  
For the HDHV database to demonstrate the identification results, three samples from each hand were chosen for training, while the remainder were used for testing. The curve of each band in Figure 9 shows that the RR first increased and then decreased after being convolved with different Gaussian filters through different local feature representation methods. To demonstrate the connection between acutance and the performance, one single band was presented in Figure 10. The RR of the single band (810 nm) from the HDHV database steadily increases before reaching a peak and then starts to sharply decrease. Therefore, there exists an optimal range based on image acutance to obtain better performances than the original image. Here, the optimal acutance range is 4.103-6.755 for different feature extractors. For example, regional LBP where the highest RR can reach 0.9667 (see Figure 10a), achieves an improvement of 7.9% compared with the original image (RR = 0.8958).  
For the HPV database, RR had the same tendency as the above (HDHV), which confirmed the hypothesis that an optimal range based on image acutance does exist (see Figure 11). With different feature descriptors, the optimal acutance range of a single band (840 nm for example in Figure 12) is 4.927-6.856, using CompCode where the highest RR can reach 0.9880 (see Figure 12a), which is an improvement of 21.8% compared with the original image (RR = 0.8110).  
Figure 8: PolyU MPP (The Hong Kong Polytechnic University Multispectral Palm Print) samples from different spectra. (**a**) Red; (**b**) Green; (**c**) Blue; (**d**) NIR.  
Figure 11: Recognition rates (RR) with different acutance (corresponding to \(\delta\)) for every band (spectrum) of HPV using different local texture patterns. Results using the feature extraction methods of (**a**) CompCode; (**b**) deep features.  
Figure 12: Recognition rates (RR) with different acutance TPVA on a single band of HPV with different local texture patterns. Results using the feature extraction methods of (**a**) CompCode; (**b**) deep features.  
Figure 10: Recognition rates (RR) with different acutance TPVA on a single band of HDHV with different local texture patterns. Results using the feature extraction methods of (**a**) regional LBP; (**b**) deep features.  
Figure 9: Recognition rates (RR) with different acutance (corresponding to \(\delta\)) for every band (spectrum) of HDHV using different local texture patterns. Results using the feature extraction methods of (**a**) regional local binary patterns (LBP); (**b**) deep features.  
For the MPP database, the RR followed the same trends as the previous two databases. This shows that there is an optimal range based on image acutance in every spectrum with different feature representation methods (see Figure 13). For CompCode as an example (see Figure 14a), the optimal acutance range of the green spectrum is 11.549-14.257, where the highest RR reached 0.9993, which is an improvement of 7.2% compared with the original image (RR = 0.9320).  
To draw conclusions more easily, the representative results of the best performing bands, in addition to the average results for all bands in each hyperspectral database are depicted in the Table 1. From this table it can be seen that for every case, after adjusting image acutance (from the optimal range) the RR increases when compared to the original ROI image, no matter which local feature extractor was used.