In the last decades, more and more documentation and data storage for research purposes was done with computers, enabling the use of algorithms to work on the data. In the 1970s, it was discovered that individual killer whales, like orcas, can be identified by their natural markings, like their fin. Re-searchers have taken pictures of killer whales to identify and document them during the last years. Each discovered orca gets a unique ID, which will be referred as “label” in the further context. This identification process is currently done manually by researchers for each picture. For his master’s the-sis “Orca Individual Identification based on Image Classification Using Deep Learning”, Alexander Gebhard developed a machine-learning pipeline that can identify individual orcas on images. If the pipeline is given a picture without labels, it will return the most likely labels for this picture. If it is given a picture with a label, then the labels and the image will be used to train the pipeline so that it can identify individual orcas better. The goal of my bachelor’s thesis is to develop and document a user-friendly web-based server-client framework (here referred as FIN-PRINT) that allows researchers to synchronize images and labels with the pipeline and to create as well as to store labels and relevant meta-data.
FIN-PRINT is a platform-independent framework that can run on different operating systems, like Win-dows, Mac OS and Linux. Users of this framework can interact with it over several webpages that are hosted locally on their computer. The interface allows the user to browse through images of orcas on their local hard-drive and to add labels to these images. They can also check automatically generated labels from the machine-learning pipeline and make corrections to the labels if necessary. Labeling new images or checking the labels from the pipeline can be done offline without an internet connec-tion if the necessary files are present. If the user has an active internet connection, they can also ex-plore statistics from a database which stores relevant data about the images and their labels, like GPS-data, the dates where an individual orca was spotted or if certain orcas were spotted together. Manu-ally labeled pictures can be uploaded to the pipeline, and automatic labels from the pipeline can be downloaded.
2.2 The Framework
The Framework has four mayor parts that interact with each other: A browser interface, a local server, an external server, and the machine-learning pipeline. The browser interface has several webpages
which are hosted locally on the local server that runs on the computer of the user. When FIN-PRINT is opened, the local server starts, and the user can access the user interface in their web browser. The local server can access the images and labels on the computer of the client and sends them to the browser. This allows the user to view the images and to label them in their browser. If the user has labeled some pictures, the browser sends the newly created labels to the local server which stores them on the hard drive. It is also possible for the user to view and check labels from the pipeline and to correct the prediction of the pipeline. If an active internet connection is available, the local server can upload new images and labels to the external server or download automatically gen-erated labels from there anytime. As an interface between the local server and the pipeline, the exter-nal server can give the local server files from the pipeline and vice versa. If the pipeline gets an unla-beled image, it identifies all orcas on the images, and returns cropped images, each of them shows the fin an orca. Every cropped image also gets its own label file. These files can be sent to the user so that they can validate the predictions of the pipeline. If the pipeline gets an image with labels, it uses it to train itself. The external server saves all data related to images and their labels in a database, like the name of the uploader and the meta-data of the images (like GPS-coordinates and time stamps). Data from this database can be used to generate statistics about the orcas. With an active internet connec-tion, the user can use the web interface to request certain information that is stored in the database.
2.3 Programming tools
 C. Bergler et al. “FIN-PRINT: A Fully-Automated Multi-Stage Deep-Learning-Based Framework for Killer Whale Individual Recognition”, Germany. FAU Erlangen-Nuernberg. Date: not published yet.
 Alexander Gebhard. “Orca Individual Identification based on Image Classification Using Deep Learn-ing”, Germany. FAU Erlangen-Nuernberg. Date: October 19, 2020.
 J. Towers et al. “Photo-identification catalogue, population status, and distribution of bigg’s killer whales known from coastal waters of british Columbia”, Canada. Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, BC. Date: 2019.
Web-Based Server-Client Software Framework for Killer Whale Indivirual Recognition
Type: BA thesis
Date: July 1, 2021 - February 1, 2022
Supervisors: Elmar Nöth,