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Abstract Tracking individuals in wild populations increasingly involves computer‐aided analyses of photographic records. While several extant tools leverage naturally occurring patterns and marks as ‘fingerprints’ to distinguish individuals, these were chiefly designed with large or vertebrate taxa in mind. Insects and other small animals present opportunities for simplified image capture and processing due to their ease of handling, manipulation and the degree to which their identifying patterns are constrained on rigid exoskeletons or wings. We present PlanarID, a Python pipeline and companion Shiny app for exploring, processing and analysing large‐scale photographic records of invertebrates. The tool identifies individuals with distinctive colour‐ or pattern‐based markings using established computer vision techniques. The pipeline applies colour‐based thresholds to annotate focal patterns on wings, shells, or elytra, which are then used to recognise individuals across photograph‐based capture–mark–recapture (CMR) datasets. This image capture workflow and pipeline aim to reduce the need for complex image pre‐processing and to provide an efficient, scalable tool for individual identification. Our Shiny app provides interactive tools to visualise all stages of ‘fingerprinting’, compare potential matches in a photographic record, quality‐check the photographic record prior to analysis, and confirm individual matches following the pipeline execution. Practical implication . We showcase PlanarID's core features using a sample photographic record of the burying beetle Nicrophorus vespilloides . We assess PlanarID's ability to assign identities to individuals using four distinct ‘fingerprinting’ algorithms. Our results indicate that PlanarID is a highly effective tool for identifying individuals via their unique, discrete colour patterns and generating capture histories from photographic records essential for further CMR analyses.