3.3 Performance of the image-matching software packages
For both the Kenyan and Zimbabwean datasets, Hotspotter achieved the highest image-matching accuracy (Figure 3). For the Kenyan dataset, using Hotspotter with crops of the full individual from which the background was removed, was most effective. This method detected 62% of the matches in the 10 highest ranked crops (Figure 3B). This was significantly higher than using manually cropped flanks of the individual in both WildID (z = 5.0, p < 0.01) and Hotspotter (z = 2.8, p = 0.046), as well as crops of the full individual in WildID (z = 4.7, p < 0.01) and I3S-Pattern (z = 5.0, p < 0.01).For the Zimbabwean dataset, Hotspotter detected 88% of matches within the first 10 ranked images when the background was removed from a crops of the full individual (Figure 3A). The matching performance was significantly lower when crops of just the flank were used (z = 2.7, p = 0.03). Hotspotter with background removal performed significantly better than WildID with background removal, (z = 4.7, p < 0.01), as well as WildID with crops of the flanks (z = 5.1, p < 0.01).
The probability of accurate image-matching occurring within the first 10 ranked images was significantly higher for wild dogs from Zimbabwe than wild dogs from Kenya (OR = 9.64, 95% CI 3.65 - 15.63, Figure 4). The proportion of matched individuals identified in this analysis was not significantly associated with image size (X21 = 0.16, p = 0.69) or image quality (ORQuality Score 2 / Quality Score 1 = 0.89, 95% C.I. -2.26 – 4.04, ORQuality Score 3 / Quality Score 1 = 1.82, 95% C.I. -2.20 – 5.83). In addition, the image quality score did not differ between the populations (W = 15008, p = 0.33).

4. Discussion

This study presents a novel framework for automating the individual recognition of species with distinct marks. The framework includes an automated pre-processing method for identifying images suitable for image-matching, and then using image-matching software for individual recognition. The automated pre-processing method consists of five steps that (1) crop all images containing animals from a large database, (2) filter out a portion of the unsuitable images based on image aspect ratio, (3) use convolutional neural nets to select images of standing individuals (accuracy of 90%), (4) separate images into left and right flanks (accuracy of 95%), and (5) remove image backgrounds. As a case study, we applied the described methods to an image catalogue of African wild dogs and found that Hotspotter (Crall et al., 2013) was the most efficient software package for matching images. Image-matching performance was also significantly improved by using the full image of an individual from which the background was removed, as opposed to just the cropped flank. Finally, we found that image-matching performance differed between populations of wild dogs with different coat coloration patterns. This work showed that image-matching software could become a powerful method for monitoring populations of African wild dogs. However, caution is needed as detection rates are likely to vary between – and even within – populations. This could affect the certainty of derived population-specific demographic parameters, such that careful consideration is needed to account for individual heterogeneity in detection when large variation in coat colouration occurs within a population.
The automated pre-processing method presented in this study could eliminate the need to manually select suitable images for image-matching and crop individuals from original photographs. This method thus enables processing of large image catalogues where selection using visual inspection would be extremely time consuming. We found that the method does discard a small number of suitable images, and therefore in situations where it is important to include all suitable images, the pre-processing method outlined here could also be used as a pre-sorting approach. The user could then visually review images that were classified as not suitable, to prevent usable images from being discarded.
The described method of pre-processing is particularly useful for wild dogs, since an individuals’ posture varies substantially between images. Images taken by tourists provide an opportunity to bolster and spatially extend image catalogues. However, these images are also likely to contain many images unsuitable for identification, as they are not taken for the purpose of identification. Accordingly, filtering unsuitable images from these datasets using an automated approach could be especially timesaving. The described pre-processing method is therefore highly suitable to species targeted by wildlife watching excursions, that have distinctive marks and where individual posture influences image suitability, for example cheetahs, leopards Panthera pardus , and tigers.
Hotspotter outperformed I3S-Pattern and Wild-ID at matching images of individual wild dogs. This finding agrees with studies on green toads that compared Hotspotter and I3S-pattern (Burgstaller, Gollmann & Landler 2021), as well as studies comparing Hotspotter and WildID (Nipko, Holcombe & Kelly, 2020; Burgstaller, Gollmann & Landler, 2021; Chehrsimin et al., 2018). Nevertheless, this result is not ubiquitous. Wild-ID was superior to Hotspotter at matching images for a blotched amphibian species, the Wyoming toad Anaxyrus baxteri (Morrison et al., 2016). This indicates that the identification performance of different software packages is dependent on species, even when two species’ patterns show similarities. Consequently, we recommend that all three software packages are tested on new species before deciding on which one to use.
Using crops of full individuals from which the background was removed significantly increased the image-matching accuracy of Hotspotter, compared to using crops of just individuals’ flanks. This method also speeds up image pre-processing by eliminating the need to manually crop the region of interest. The improved accuracy is likely caused by two factors. Firstly, removing the background prevents images being matched based on similar backgrounds, as the flanks are not perfect rectangles, meaning that crops of the flank also contain some background (see Figure S2). Secondly, using complete individuals allows images to be matched based on unique features on the legs, in addition to the flanks. This result is in line with studies on Saimaa ringed seals Pusa hispida and Thornicroft’s giraffes Giraffa camelopardalis thornicrofti , which found evidence that using a full individual from which the background is removed, could result in a higher accuracy (Chermin et al., 2018; Halloran, Murdoch & Becker 2015). However, neither of these previous studies statistically tested whether background removal increased identification accuracy. Our study therefore provides the first statistical evidence that background removal can increase the performance of image-matching software. This also indicates that the common usage of Hotspotter, in which a rectangular region of interest is manually cropped (e.g. Dunbar et al. , 2021; Nipko, Holcombe & Kelly, 2020), could be improved by removing the image background.
Hotspotter was significantly better at matching images from Zimbabwean wild dogs, compared to Kenyan individuals. The higher image-matching accuracy found for the Zimbabwean population is likely to reflect the regional difference in wild dog coat colouration patterns. The Kenyan population has darker, more uniform coats, consisting of large black patches, often with few white or tan areas (McIntosh, Woodroffe & Rabaiotti, 2016, Daniels, Woodroffe & Rabaiotti, 2022). By contrast, the proportion of tan fur is ~1.5 times higher, and the proportion of white fur is almost 7 times higher for the Zimbabwean population (Figure 1, Daniels, Woodroffe & Rabaiotti, 2022). Therefore, the higher contrast within the patterns of the Zimbabwean wild dogs could make it easier for the software to match images of these individuals. The identified relationship between image-matching performance and software package remained unaltered when image quality and image size were included in analyses, and there was no significant difference between the image quality scores between the Zimbabwean and Kenyan populations. The image quality score approach was modelled after Nipko, Holcombe & Kelly (2020), who found that it significantly affected the probability of matching ocelot and jaguar individuals. As a result, we are confident that the differences in coat colouration patterns between wild dogs from Zimbabwe and Kenya reflect variation in identification performance between populations.
Inter-population variation in image-matching performance indicates that detection probabilities derived from using this approach will not be directly comparable between populations. Since the probability of finding an accurate image-match depends on individual coat pattern, this finding highlights that individual heterogeneity in detection may also occur if large variation in coat colouration occurs within a population. Capture-mark-recapture techniques assume individuals experience equal detection probability across a population (White and Burnham, 2009). Therefore, individual coat pattern may also need accounting for when deriving survival estimates using such analysis. This also applies to other species whose coat pattern varies regionally, such as Asian golden cats Catopuma temminckii and ocelots (Allen et al., 2011; Khan, Ali & Mohammed, 2017). Furthermore, the coat patterns of other wild dog populations can differ considerably from the two populations included in this study (McIntosh, Woodroffe & Rabaiotti, 2016, Daniels, Woodroffe & Rabaiotti, 2022). Consequently, we advocate that estimating a population-specific image-matching accuracy score becomes an essential pre-requisite step for applying these techniques in different locations.
Automatically pre-processing wild dog image datasets and using image-matching software facilitates the use of archived and citizen science image catalogues where visually identifying all individuals would be extremely time-consuming. Although the best performing image-matching software did not detect all matches, it could be used to identify a large proportion of the individuals in a dataset. Afterwards, individuals that were not matched to any other images could be visually identified, to prevent missing actual matches. Using image-matching software in this way still saves time by rapidly identifying a large portion of the matches, without compromising on accuracy. Furthermore, it is plausible that the likelihood of correctly detecting matching images increases if more than two images per individual are included, for example if multiple viewpoints per individual are present in a dataset, the probability of matching these is expected to increase (Crall et al., 2013). Our accuracy values therefore represent a conservative estimate of Hotspotter’s true accuracy.
Our study indicates that image-matching could provide a valuable new approach for monitoring wild dogs. A combination of citizen science and image-matching has already been successfully employed to monitor other species, such as Blanding’s turtles Emydoidea blandingii and whale sharks (Araujo et al. , 2017; Cross et al. , 2021). Similarly, previous studies have used tourist images to estimate the population size of wild dogs in Kruger National Park, South Africa (Marnewick et al., 2014). Combining citizen science, image-matching software, and capture-recapture methods therefore has the potential to improve understanding of wild dog demography. However, more research is needed to investigate whether photographic data could improve our understanding of wild dog demography beyond population size, by estimating parameters such as pack structure, dispersal rates, and death and birth rates. This can be achieved by applying image-matching software to existing image datasets, to assess whether they generate enough data to estimate key demographic parameters, or whether more intensive monitoring - for example using long term camera trap surveys - would be necessary.
In conclusion, we have developed a new automated method for pre-processing image datasets, by automatically cropping animals from images, removing images in which the individuals’ posture hinders identification, separating left and right flanks, and removing the image background. This framework will enable large image datasets to be analysed rapidly, thereby expanding monitoring efforts and expediting conservation action. Furthermore, we have shown how well different image-matching software packages perform on African wild dogs. Hotspotter outperformed the other software packages, while its performance differed between two populations which exhibit intra-specific variation in their coat patterns. Our pre-processing method, in combination with Hotspotter, has immediate application in research and monitoring efforts for wild dogs and other species. Data obtained in this way could provide cost-effective large-scale monitoring for endangered species, therefore supporting the implementation of effective conservation.