Reliable Detection of Doppelg\'angers based on Deep Face Representations
| Autor: | Rathgeb, Christian, Fischer, Daniel, Drozdowski, Pawel, Busch, Christoph |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Doppelg\"angers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non-mated comparison trials. In this work, we assess the impact of doppelg\"angers on the HDA Doppelg\"anger and Disguised Faces in The Wild databases using a state-of-the-art face recognition system. It is found that doppelg\"anger image pairs yield very high similarity scores resulting in a significant increase of false match rates. Further, we propose a doppelg\"anger detection method which distinguishes doppelg\"angers from mated comparison trials by analysing differences in deep representations obtained from face image pairs. The proposed detection system employs a machine learning-based classifier, which is trained with generated doppelg\"anger image pairs utilising face morphing techniques. Experimental evaluations conducted on the HDA Doppelg\"anger and Look-Alike Face databases reveal a detection equal error rate of approximately 2.7% for the task of separating mated authentication attempts from doppelg\"angers. Comment: accepted in IET Biometrics |
| Databáze: | arXiv |
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