Face memory is an essential cognitive ability for social interactions, yet individuals show considerable variation in this skill. This study presents the development of EGEFACE, a face memory test designed to address limitations of existing assessments by integrating features such as static and dynamic stimuli, adjusting difficulty using a face recognition algorithm, and including both target-present and target-absent trials to reduce response bias. EGEFACE employs ROC analysis to evaluate face memory accuracy independent of response bias. EGEFACE was developed using a newly constructed database of faces and calibrated across three difficulty levels using similarity scores derived from a face recognition algorithm. Data from 703 participants, collected in two stages, demonstrated acceptable internal consistency and split-half reliability. Moderate correlations with the CFMT+ suggest EGEFACE assesses similar cognitive processes, while weaker correlations with the KFMT indicate it captures distinct aspects of face recognition. High performers in EGEFACE achieved scores comparable to super recognizer level on the CFMT+, whereas low performers showed minimal overlap with prosopagnosic-level scores. These findings indicate that EGEFACE has the potential to serve as a useful tool for assessing individual differences in face memory, offering an alternative to existing tests while addressing some of their limitations.