Nowadays, almost anyone can take pictures at any time. Simultaneously, services such as social networks make it easy to share and redistribute these images. Users who do not want pictures of them to be recorded and distributed can hardly defend themselves against this. With the introduction of the GDPR in the European Union, users can now at least demand the deletion of such unsolicited uploaded data from web platforms. To find such images, however, the user must first upload comparative images to such a web service so that this service can compare them with its database to show the user whether unwanted images exist or not. This means that the user must involuntarily pass on his biometric data to a web service where he does not actually want his data to be saved. Thus, in this paper, we present our privacy-friendly face recognition approach based on Local Binary Patterns and Error Correction Codes, that allows users to query web services for the presence of unwanted images without revealing biometric information. We evaluated each step of our approach with the “FERET database of facial images” and the “Yale Face Database”.