Image-Based Automatic Individual Identification of Fish without Obvious Patterns on the Body (Scale Pattern)  
[PERSON]  
Laboratory of Signal and Image Processing, Institute of Complex Systems, Faculty of Fisheries and Protection of Waters, CENAKVA, University of South Bohemia in Ceske Budejovice, Zimek 136, 373 33 Nove Hrady, Czech Republic; [EMAIL_ADDRESS]  
[PERSON]  
Laboratory of Signal and Image Processing, Institute of Complex Systems, Faculty of Fisheries and Protection of Waters, CENAKVA, University of South Bohemia in Ceske Budejovice, Zimek 136, 373 33 Nove Hrady, Czech Republic; cisar@frovj. with significant differences to other species due to challenging aquaculture conditions. Identification is a standard research approach as it allows one to individualize the fish. Currently, there are very limited options for the real-time identification of individuals under real conditions (in tanks and cages). Solving the issue of real-time individual identification under real-world conditions would open a new area of research for individual fish treatment. Our study was focused on testing if the non-invasive (photo) identification of individual fish without obvious pattern could work, and moreover, on testing the stability of chosen features on a fish body during the cultivation period. The most widely used method for individual-fish identification is invasive tagging [8]. There are many negative impacts of tagging, such as being traumatic for the fish (as it is an invasive method), increasing mortality and injury, being a time consuming procedure, applicability to limited fish sizes, and the need to catch fish for tagging and identification [9; 10]. Modern methods of individual-fish identification based on non-invasive principles, such as photo identification, can minimize all the negative consequences of tagging. Photo-recognition is often used not only as a tool to track morphological changes and behavioral monitoring but also for individual identification [11]. The principle of photo recognition has already been successfully used for fish species identification. Fish species identification methods are very broad, but the main criteria for classification are morphological and meristic features [12; 13]. Several species identification systems were developed for use under real conditions and in real time [14; 15; 16]. At present, the most popular method for species identification is the deep learning approach [17; 18]. This approach has been very successful, but the limitations of this approach include the need for a large number (hundreds/thousands) of training examples. The identification of individuals of the same species is a more challenging task than the identification of species. Typically, few images of an individual are available, and the similarities within one species are much higher than the similarities between species. Several studies demonstrated that non-invasive photo-based identification of individual fish is possible for different fish species, such as catshark (_Scylidininus canalar_) [19], zebrafish (_Danio rerio_) [20], Eurasian perch (_Perca fluviatilis_) [21], common carp (_Cypinus carpio_) [22], Atlantic salmon (_Salmo salar_) [23], brown trout (_Salmo tutta_) [24], whale shark, white shark, and spotted-edge ray [25]. The stability of the chromatophore and melanophore patterns of individuals has also been studied [23; 26]. Identification approaches are based on visible patterns on the fish's body (stripes, dots, iris of the eye). Previous studies were performed with relatively low numbers of individual (maximum 30) but demonstrated that skin and eye patterns are unique and can be used for identification. It was also confirmed that these patterns are stable for reasonable time periods (several months) of fish cultivation. The common factor of these studies is the use of manual or semi-automatic image processing. This means that identification must be performed by human experts, which is time consuming and not practical for automation in commercial aquaculture.