# Photo Identification of Individual _Salmo trutta_ Based on Deep Learning
## 3 Results  
We presented the baseline results of the identification method on multiple experimental setups and compared them with histogram of oriented gradient (HOG) [47], rotation invariant local binary pattern (LBP) [48] feature-based methods. For each fish, HOG and LBP features were extracted and the histogram intersection was used to train a linear support vector machine classifier [49]. The HOG features were computed with eight orientations with 32 pixels per block, giving 2048 feature dimension. Thirty six rotation invariant LBP features were computed with a radius of 3 at each pixel and the resulting texture image was represented with a histogram of 512 dimension. The identification method was performed in multiple stages. First, we evaluated the method for geometric variations such as flipping and rotation. To do so, given an image and its transformation, we predicted the performance whether the two brown trouts were identical or not. For the NINA204 dataset, an F1-score of 0.974 was obtained for \(T+0\) and the F1-score decreased to 0.832 for \(T+5\) (Table 1). Our proposed method performed better than HOG and LBP at all times in all performance metrics.  
Furthermore, we evaluated the identification method on the TROUT39 dataset. The TROUT39 dataset was significantly different from NINA204, as the pictures were taken in open-air with a high-definition camera. We obtained an overall performance of 0.592 in the F1-score (Table 2). Sample visual results are given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time + \(\Delta t\)** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **LR+** \\ \hline \multirow{3}{*}{\(T+0\)} & Proposed & 0.974 & 0.980 & 0.967 & 0.974 & 0.981 & 50.894 \\  & HOG+SVM [47] & 0.807 & 0.771 & 0.877 & 0.821 & 0.735 & 3.309 \\  & LBP+SVM [48] & 0.804 & 0.904 & 0.684 & 0.779 & 0.926 & 9.24 \\ \hline \multirow{3}{*}{\(T+1\)} & Proposed & 0.967 & 0.978 & 0.954 & 0.966 & 0.980 & 47.7 \\  & HOG+SVM [47] & 0.779 & 0.715 & 0.856 & 0.779 & 0.715 & 3.0 \\  & LBP+SVM [48] & 0.788 & 0.858 & 0.640 & 0.734 & 0.912 & 7.27 \\ \hline \multirow{3}{*}{\(T+2\)} & Proposed & 0.951 & 0.973 & 0.924 & 0.948 & 0.976 & 38.5 \\  & HOG+SVM [47] & 0.761 & 0.715 & 0.813 & 0.761 & 0.716 & 2.86 \\  & LBP+SVM [48] & 0.754 & 0.841 & 0.583 & 0.688 & 0.904 & 6.07 \\ \hline \multirow{3}{*}{\(T+3\)} & Proposed & 0.917 & 0.966 & 0.848 & 0.903 & 0.975 & 33.92 \\  & HOG+SVM [47] & 0.752 & 0.691 & 0.778 & 0.732 & 0.731 & 2.89 \\  & LBP+SVM [48] & 0.780 & 0.869 & 0.584 & 0.699 & 0.932 & 8.59 \\ \hline \multirow{3}{*}{\(T+4\)} & Proposed & 0.903 & 0.961 & 0.813 & 0.881 & 0.974 & 31.27 \\  & HOG+SVM [47] & 0.744 & 0.680 & 0.766 & 0.720 & 0.727 & 2.80 \\  & LBP+SVM [48] & 0.775 & 0.847 & 0.582 & 0.690 & 0.920 & 7.27 \\ \hline \multirow{3}{*}{\(T+5\)} & Proposed & 0.874 & 0.946 & 0.743 & 0.832 & 0.969 & 23.96 \\  & HOG+SVM [47] & 0.725 & 0.658 & 0.726 & 0.691 & 0.725 & 2.64 \\ \cline{1-1}  & LBP+SVM [48] & 0.757 & 0.833 & 0.529 & 0.647 & 0.923 & 6.87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TROUT39 dataset recognition performance. Note that the unmatched pairs were randomly generated for evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **LR+** \\ \hline Proposed & 0.718 & 0.766 & 0.483 & 0.592 & 0.891 & 1.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fish recognition performance vs. temporal distance in NINA204 test dataset. Each fish was compared with frames containing the same fish taken at different time step \(\Delta t\) (frames).