It is often challenging to use invasive methods of individual animal identification for population estimation, demographic analyses, and other ecological and behavioral analyses focused on individual-level processes. Recent improvements in camera traps make it possible to collect many photographic samples yet most investigators either leap from photographic sampling to assignment of individual identity without considering identification errors, or else to avoid those errors they develop computerized methods that produce accurate data with the unintended cost of excluding participation by local citizens. To assess human ability to visually identify Andean bears Tremarctos ornatus from their pelage markings we used surveys and experimental testing of 381 observers viewing photographs of 70 Andean bears of known identity. Neither observer experience nor confidence predicted their initial success rate at identifying individuals. However, after gaining experience observers were able to achieve an average success at identifying adult bears of 73.2%, and brief simple training further improved the ability of observers such that 24.8% of them achieved 100% success. Interestingly, observers who were initially more likely to falsely identify two photos of the same bear as two different bears than vice versa were likely to continue making errors and their bias became stronger, not weaker. Such biases would lead to inaccurate population estimates, invalid assessments of the bears involved in conflict situations, and underestimates of bear movements. We thus illustrate that in some systems accurate data on individual identity can be generated without the use of computerized algorithms, allowing for community engagement and citizen science. In addition, we show that when using observers to collect data on animal identity it is important to consider not only the overall frequency of observer error, but also observer biases and error types, which are rarely reported in field studies.