Visual identification of individual animals that bear unique natural body markings is an important task in wildlife conservation. The photo databases of animal markings grow larger and each new observation has to be matched against thousands of images. Existing photo-identification solutions have constraints on image quality and appearance of the pattern of interest in the image. These constraints limit the use of photos from citizen scientists. We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination changes. We adapt methods developed for face re-identification and implement a deep convolutional neural network (CNN) to learn embeddings for images of natural markings. The distance between the learned embedding points provides a dissimilarity measure between the corresponding input images. The network is optimized using the triplet loss function and the online semi-hard triplet mining strategy. The proposed re-identification method is generic and not species specific. We evaluate the proposed system on image databases of manta ray belly patterns and humpback whale flukes. To be of practical value and adopted by marine biologists, a re-identification system needs to have a top-10 accuracy of at least 95 . The proposed system achieves this performance standard.
Some methods have been developed for specific species and, while performing well on these, are not easily transferable to other species. High-contrast colour patterns of humpback whale flukes @cite_8 and dolphin dorsal fins @cite_41 are matched by extracting hand-crafted features from corresponding segments obtained by overlaying a grid on a region of interest. This method is not robust to viewpoint changes.
@cite_41 @cite_8
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This paper describes current work on a photo-id system for humpback whales. Individuals of this species can be uniquely identified by the light and dark pigmentation patches on their tails (flukes). We developed an interface that assists the user in segmenting the animal's tail from the sea and fitting an affine invariant coordinate grid to it. A numerical feature vector capturing the patch-distribution with respect to the grid is then automatically extracted and used to match the individual against a database of similarly processed images. We address the problem of identifying individual cetaceans from images showing the trailing edge of their fins. Given the trailing edge from an unknown individual, we produce a ranking of known individuals from a database. The nicks and notches along the trailing edge define an individual's unique signature. We define a representation based on integral curvature that is robust to changes in viewpoint and pose, and captures the pattern of nicks and notches in a local neighborhood at multiple scales. We explore two ranking methods that use this representation. The first uses a dynamic programming time-warping algorithm to align two representations, and interprets the alignment cost as a measure of similarity. This algorithm also exploits learned spatial weights to downweight matches from regions of unstable curvature. The second interprets the representation as a feature descriptor. Feature keypoints are defined at the local extrema of the representation. Descriptors for the set of known individuals are stored in a tree structure, which allows us to perform queries given the descriptors from an unknown trailing edge. We evaluate the top-k accuracy on two real-world datasets to demonstrate the effectiveness of the curvature representation, achieving top-1 accuracy scores of approximately 95 and 80 for bottlenose dolphins and humpback whales, respectively. Ecologists commonly use photo-identification of individual animals to monitor the behaviour, state and health of a population, since it is a cost-effective technique that eliminates the need to physically capture and tag animals. With dolphins, the nicks and notches of the dorsal fin are typically used as the unique identifying features for each individual; however New Zealand common dolphins are relatively unmarked, so most of the population cannot be identified. Here, we investigate how computer vision can be used to extract information from the pigmentation patterns that are typically seen on adult common dolphin dorsal fins. We develop features that are relatively robust to changes in the fin orientation and compare the classification rates of 779 photos of 169 different adult common dolphins. Using pigmentation-based features, we correctly classified individuals 75 of the time, with our top-5 estimates containing the correct dolphin in 86 of the cases. Summary 1 The ability to identify individual animals is a critical aid in wildlife and conservation studies requiring information on behaviour, distribution, habitat use, population and life-history parameters. We present a computer-aided photo-identification technique that relies on natural marks to identify individuals of Carcharias taurus, a shark species that is critically endangered off the eastern Australian coast and considered globally vulnerable. The technique could potentially be applied to a range of species of similar form and bearing natural marks. 2 The use of natural marks for photo-identification is a non-invasive technique for identifying individual animals. As photo-identification databases grow larger, and their implementation spans several years, the historically used visual-matching processes lose accuracy and speed. A computerized pattern-matching system that requires initial user interaction to select the key features aids researchers by considerably reducing the time needed for identification of individuals. 3 Our method uses a two-dimensional affine transformation to compare two individuals in a commonly defined reference space. The methodology was developed using a database of 221 individually identifiable sharks that were photographically marked and rephotographed over 9 years, demonstrating both the efficacy of the technique and that the natural pigment marks of C. taurus are a reliable means of tracking individuals over several years. 4 Synthesis and applications. The identification of individual animals that are naturally marked with spots or similar patterns is achieved with an interactive pattern-matching system that uses an affine transformation to compare selected points in a single-user computer-aided interface. Our technique has been used successfully on C. taurus and we believe the methodology can be applied to other species of a similar form that have natural marks or patterns. The identification of individuals allows accurate tracking of their movements and distribution, and contributes to better population estimates for improved wildlife management and conservation planning.