Open AccessComparison of photo‐matching algorithms commonly used for photographic capture–recapture studies
Author(s) -
Matthé Maximilian,
Sannolo Marco,
Winiarski Kristopher,
Spitzen van der Sluijs Annemarieke,
Goedbloed Daniel,
Steinfartz Sebastian,
Stachow Ulrich
Publication year - 2017
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.3140
Subject(s) - computer science , matching (statistics) , database , algorithm , ranking (information retrieval) , identification (biology) , pixel , artificial intelligence , mark and recapture , population , pattern recognition (psychology) , data mining , mathematics , statistics , demography , sociology , botany , biology
Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state‐of‐the‐art photo‐matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo‐matching algorithms; Wild‐ ID , I3S Pattern+, APHIS , and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel‐based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates.