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Doppelg\"angers (or lookalikes) usually yield an increased probability offalse matches in a facial recognition system, as opposed to random face imagepairs selected for non-mated comparison trials. In this work, we assess theimpact of doppelg\"angers on the HDA Doppelg\"anger and Disguised Faces in TheWild databases using a state-of-the-art face recognition system. It is foundthat doppelg\"anger image pairs yield very high similarity scores resulting ina significant increase of false match rates. Further, we propose adoppelg\"anger detection method which distinguishes doppelg\"angers from matedcomparison trials by analysing differences in deep representations obtainedfrom face image pairs. The proposed detection system employs a machinelearning-based classifier, which is trained with generated doppelg\"anger imagepairs utilising face morphing techniques. Experimental evaluations conducted onthe HDA Doppelg\"anger and Look-Alike Face databases reveal a detection equalerror rate of approximately 2.7% for the task of separating matedauthentication attempts from doppelg\"angers.

Reliable Detection of Doppelg\"angers based on Deep Face Representations