# A New Enhancement Edge Detection of Finger-Vein Identification for Computer System
## 4 Experimental Results and Analysis
### Setting and Evaluation  
Unsuitable parameters hinder the model's performance on systems that use DL so minor details affect the final result. This study separates the FV-USM and SDUMIA-HMT datasets into training and testing data sets. The training dataset is used to train and test the model and the testing dataset is used for verification. The AlexNet optimization component uses an Adam optimizer with a learning rate of 0.001, a batch size of 64 and 600 epochs. In recent years, the security of biometric systems has been the subject of much interest. The definition of analysis of variances (ANOVA) test and T-test refers to the degree of difference between samples [20; 21]. This is not suitable for finger-vein identification that each biometric is corresponding to the person individual. However, a critical index for the safety of 1:\(N\) identification systems is the error equal rate (EER). Identification systems incur two types of errors: the false rejection rate (FRR), whereby the system identifies a registered user as unregistered, as shown in Equation (7), and the false acceptance rate (FAR), whereby the system mistakes an unregistered user as registered, as shown in Equation (8). The security of the device is adjusted from the most to least secure by fine-tuning the system threshold. Changing the threshold increases the FAR value, until the probability approaches 1. The FRR value decreases to 0 and increases with the FAR. The EER is the intersection of the FRR and FAR curves. The threshold value is adjusted for different applications and scenarios but setting it at the EER value provides the most balanced performance. The EER is the index that denotes the system's level of security. \[FRR=\frac{FN}{TP+FN} \tag{7}\]  
\[FAR=\frac{FP}{FP+TN} \tag{8}\]  
This study proposes DL training for testing with edge detection and various image enhancement methods. Three different experiments give a range of analysis for future applications. Three finger-vein images were randomly selected from the dataset for training, and three were selected for testing. This is a 3T/3T setup. 4T/2T and 5T/1T setups were also used with different combinations of testing and training images. The HE using a wavelet transform (WT) [22] was used for these testing sets, as shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**3T/3T** & **EER** & **Accuracy Rate** & **Memory Size** \\ \hline WT + DL & 0.007\% & 96.7\% & 5349 Mib \\
**This work** & **0.003\%** & **97.9\%** & **5514 Mib** \\ \hline
4T/2T & EER & Accuracy rate & Memory size \\ WT + DL & 0.005\% & 98.4\% & 5814 Mib \\
**This work** & **0.002\%** & **99.0\%** & **4467 Mib** \\ \hline
5T/1T & EER & Accuracy rate & Memory size \\ WT + DL & 0.004\% & 98.0\% & 5908 Mib \\
**This work** & **0.002\%** & **99.1\%** & **5560 Mib** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stability of identification for three vein image analysis scenarios using the FV-USM dataset. Figure 9: Vein image from the SDUMIA-HMT dataset [19]. For the contact-based FV-USM dataset, 3T/3T vein image analysis using HE and DL gives the lowest EER value of 0.003 and highest accuracy of 97.9% because HE gives a more stable luminance contrast than WT-based image processing. For 4T/2T analysis, the lowest EER value is 0.002 and the highest accuracy is 99.0%. For 5T/1T analysis, the lowest EER value is 0.002 and the highest accuracy is 99.1%. Experiments show that the device is stable for 4T/2T and 5T/1T image analysis trials, as shown in Table 1.  
To measure the effectiveness of the proposed enhancement edge detection method in processing images for contactless biometrics, this study uses the contactless-based SDUMLA-HMT public dataset for testing. For 3T/3T testing using HE and DL training, the EER is 0.010 and the accuracy is 96.0% because WT increases the brightness of excessively dark regions through the low-low band. However, 5T/1T is the most common training and testing setup for practical applications.