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This paper discusses the human-biometric-sensor interaction (HBSI) evaluation method that uses ergonomics, usability, and sample quality criteria as explanatory variables for the overall biometric system performance. The HBSI method was proposed because of questions regarding the thoroughness of traditional system-level performance evaluation metrics such as the failure-to-acquire (FTA) rate, the failure-to-enroll (FTE) rate, the false-accept rate (FAR), and the false-reject rate (FRR). Data were collected from 85 individuals over three visits that accounted for 25 867 user interactions with three swipe-based fingerprint sensors. The results in this paper revealed that traditional biometric evaluations that focus on system-level metrics are not providing sufficient reporting details regarding the user interaction with the devices. In this paper, the systemic FTA rate of 14.38% was shown to be segmented into three metrics: false interaction (FI), failure to detect (FTD), and concealed interaction (CI). The results show that the FI accounted for 69.05% of the systemic FTA presentations, FTD accounted for 30.71%, and CI accounted for 0.24%. Overall, the HBSI evaluation method and framework for biometric interactions provided new metrics that improve the analysis capabilities for biometric performance evaluations as it links system feedback to the human-sensor interaction, enabling researchers, system designers, and implementers to understand if the issues are the result of the system, the user, both the system and the user, or some other extraneous factor.