The field of automated face verification has become saturated in recent years, with state-of-the-art methods outperforming humans on all benchmarks. Many researchers would say that face verification is close to being a solved problem. We argue that evaluation datasets are not challenging enough, and that there is still significant room for improvement in automated face verification techniques. This paper introduces the DoppelVer dataset, a challenging face verification dataset consisting of doppelganger pairs. Doppelgangers are pairs of individuals that are extremely visually similar, oftentimes mistaken for one another. With this dataset, we introduce two challenging protocols: doppelganger and Visual Similarity from Embeddings (ViSE). The doppelganger protocol utilizes doppelganger pairs as negative verification samples. The ViSE protocol selects negative pairs by isolating image samples that are very close together in a particular embedding space. In order to demonstrate the challenge that the DoppelVer dataset poses, we evaluate a state-of-the-art face verification method on the dataset. Our experiments demonstrate that the DoppelVer dataset is significantly more challenging than its predecessors, indicating that there is still room for improvement in face verification technology.

REU Site: Collaborative Human-Robot Interaction for Robots in the Field, National Science Foundation PI: David Feil-Seifer, co-PI: Emily Hand, Amount: $405,000, March 1, 2022 - Feb. 28, 2025