Gender information is often absent from databases available to scholars, thus hindering the proper problematization, investigation, and answering of various gender-related research questions. Named-based algorithms represent the most simple, yet effective used gender detection methods: such methods proceed by generating first-name-to-gender mapping tables based on user records in a given dataset and then applying such mapping tables "in reversal" to other databases for completion or validation purposes. The present research aims to develop a gender detection algorithm focusing on the gender detection of eponymous Wikipedia pages and compare its performance to that of other well-known gender detection databases, using the author names indexed in the Web of Science.