# Identification of Shark Species Based on Their Dry Dorsal Fins through Image Processing
## 4 Discussion  
The results obtained in this study show that the non-linear composite phase filter can successfully correlate (100%) with 37 different species of dry shark dorsal fins. In this context, the results obtained were similar to those obtained using species-specific composite filters to identify the dry fins (dorsal fins, right-sided pectoral fins, and caudal fins) of three shark species: _Prionace glauca_, _Isurus oxyrinchus_, and _Lamna nasus_. A 100% identification was recorded among the fins of each species analyzed [36]; however, in the study of [36], an inverse Gaussian filter was used to enhance the high frequencies, and the technique in [34] was used to have rotation invariance and the confidence level was calculated (95.4%). Only a phase filter was used in this study, and the percentages for the sensitivity and specificity were calculated. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scientific Name** & **Dry Dorsal Fins with Noise** & **\% Sensitivity** & **\% Specificity** \\ \hline _Sphyrna leviini_ & 459 & 91.28\% & 98.65\% \\ _Sphyrna zygaena_ & 113 & 66.37\% & 99.35\% \\ _Sphyrna mokaran_ & 104 & 82.69\% & 99.66\% \\ _Lamna nasus_ & 101 & 90.09\% & 99.63\% \\ _Carchardon carcharias_ & 101 & 96.03\% & 99.68\% \\ _Carcharhinus longimanus_ & 105 & 92.38\% & 99.66\% \\ _Carcharhinus falciformis_ & 130 & 77.69\% & 99.25\% \\ _Alopias vulipinus_ & 113 & 86.72\% & 99.63\% \\ _Alopias pelagicus_ & 138 & 78.98\% & 99.40\% \\ _Alopias superciliosus_ & 173 & 86.70\% & 99.45\% \\ _Isurus oxyrinchus_ & 116 & 81.03\% & 99.22\% \\ _Isurus pancus_ & 103 & 98.05\% & 99.81\% \\ _Rhincodon typus_ & 105 & 98.05\% & 99.92\% \\ _Carcharhinus acronotus_ & 100 & 95\% & 99.89\% \\ _Carcharhinus brachyurus_ & 101 & 95.04\% & 99.94\% \\ _Carcharhinus brevijnna_ & 105 & 74.28\% & 99.61\% \\ _Carcharhinus isodon_ & 102 & 92.15\% & 99.89\% \\ _Carcharhinus leucas_ & 109 & 79.81\% & 99.58\% \\ _Carcharhinus limbatus_ & 102 & 75.49\% & 99.23\% \\ _Carcharhinus obscurus_ & 104 & 88.46\% & 99.33\% \\ _Carcharhinus perezi_ & 104 & 96.15\% & 99.58\% \\ _Carcharhinus plumbeus_ & 102 & 100\% & 99.94\% \\ _Carcharhinus sealei_ & 104 & 92.30\% & 99.79\% \\ _Carcharhinus signatus_ & 103 & 97.08\% & 99.79\% \\ _Carcharhinus taurus_ & 108 & 88.88\% & 99.79\% \\ _Galecerdo cuvier_ & 117 & 71.79\% & 99.92\% \\ _Gingylmostona cirratum_ & 101 & 96.03\% & 99.71\% \\ _Gingylmostona unumi_ & 100 & 99\% & 99.79\% \\ _Mustelus lmutalus_ & 100 & 94\% & 99.92\% \\ _Museltus mustelus_ & 100 & 94\% & 99.94\% \\ _Negaprion acutidens_ & 100 & 97\% & 99.92\% \\ _Negaprion brevirostris_ & 100 & 98\% & 99.89\% \\ _Prionace glauca_ & 100 & 81\% & 99.30\% \\ _Rhizopriondon acutus_ & 100 & 99\% & 99.89\% \\ _Rhizopriondon lugugurio_ & 100 & 85\% & 99.87\% \\ _Sphyrna tihuro_ & 100 & 92\% & 99.92\% \\ _Triandon obesus_ & 100 & 96\% & 99.89\% \\ Random group & 115 & 85.21\% & 99.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sensitivity and specificity percentage of each dry dorsal fin shark species using the neural network with 89% efficiency. This table represents the database of dry shark fins with noise in the background of the images. A non-linear compound filter uses this value, \(k\), as the non-linear operator. By changing the value to 1, we obtain a classically matched filter that has the advantage of optimizing the output when the input signal (image problem) is degraded by additive white noise [33]. When \(k=0\), we have a phase-only filter that maximizes the light efficiency in an optical system; moreover, when \(k=-1\), we have an inverse filter that minimizes the correlation energy criteria. This last filter produces a narrower peak in the output correlation plane if the reference image and problem image are the same [34]. When the non-linear operator modifies the Fourier transform of the problem and reference images, we consider that we have a non-linear processor.