## 5 Results and Discussion
### Visualize the CNN Decision  
In order to make our verification results more explainable. We generate an attention saliency map over the questioned sub-image block, which is based on the last dense layer (fully-connected layer) outputs in our networks. For clearer visualization, we use the jet-colormap to represent the intensity then impose the saliency map on the original signature image, see Figure 5a. The jet-colormap returns the colors contain blue, green, yellow, and red. It represents the values of saliency (between 0-1), the color scheme as shown in Figure 5b.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Network** & & & & & & & & & & & & \\
**Architecture** & & & & & & & & & & & & & \\ \hline
**Genuine Author ID** & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**Experiment No.**} & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** & **Exp.** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Forged author Num. & 3 & 2 & 1 & 3 & 2 & 1 & **3** & 2 & 1 & **3** & 2 & 1 \\ Forged signature Num. & 6 & 4 & 2 & 6 & 4 & 2 & **6** & 4 & 2 & **6** & 4 & 2 \\ Training accuracy (\%) & 99.78 & 100 & 100 & 100 & 99.88 & 99.98 & **99.93** & 100 & 99.67 & **100** & 99.97 & 100 \\ Validation accuracy (\%) & 99.42 & 97.66 & 100 & 89.97 & 94.26 & 84.58 & **100** & 100 & 95.66 & **98.96** & 97.56 & 99.98  
In Figure 5, the images are from authors No. 14 and No. 16. The network architecture is the higher-performance Inception V3 network and trained by a genuine author and three forged authors (i.e., the main experimental group: Exp. 7 and Exp. 10). These attention saliency maps are able to provide some insights into our deep CNN network. First of all, we can confirm that the verification results of our network are derived from the signature itself (rather than other unrelated noise). Furthermore, by observing the significant areas, it can show which strokes are important for the networks decision (i.e., the highly saliency areas). For example, we find that the turning point and the intersection of the strokes are often used as an important basis for signature verification.  
Compared to the saliency maps of other general image classification networks, our saliency maps rarely show high saliency areas (marked in red). This is in line with the experimental design of this paper because our networks use the only single known genuine signature for training. Considering that each individual author's signatures have high intra-class variability, in general, we need a certain number of signatures to extract the stable and deterministic features from them. Therefore, networks trained by a single signature are \"not\" enough to extract this kind of feature. Also, our work focuses on verifying by using local features with signature's sub-image blocks. On the other hand, we want to avoid such extreme features from dominating the whole network decision.  
Figure 5: Saliency maps for the output layer of CNN networks. (**a**) Attention saliency maps for the signature sub-image blocks. (**b**) The colormap contains values between 0 and 1.