Responsible deployment of face verification systems requires more than accurate decisions: systems should also provide interpretable and auditable evidence that enables users to understand, assess, and challenge their decisions. Vision-language models (VLMs) provide a promising foundation for explainable face recognition by combining visual analysis with natural-language reasoning. However, relying on a single model may further limit the decision accuracy as well as provided explanations. This work therefore investigates whether multiple VLMs can be combined to improve recognition accuracy, and to enrich the explanations associated with those decisions. This work evaluates four VLMs as standalone face verification systems and subsequently proposes a fusion framework, where two source models provide similarity scores and textual justifications and a third VLM acts as a decider model. Four different fusion scenarios are considered, progressively providing the decider model with scores, justifications, face images, and combinations of these modalities. Overall, the findings suggest that the value of multi-VLM fusion extends beyond recognition performance. VLMs can provide complementary justifications and perspectives that enable richer explanations of face recognition decisions, supporting greater transparency, auditability, and error analysis. This is relevant to the development of responsible explainable face verification systems, where users and operators should be able to understand not only the final decision but also the evidence and potential sources underlying it. The proposed multimodal VLM, which combines decision scores, explanations, and face images, achieves higher recognition accuracy than state-of-the-art VLMs and domain-specific face recognition models, while also providing fused explanations that are expected to be more robust than those generated by individual VLMs.
TABLE I: Best performing VLMs evaluated for FR in recent benchmarks.
Model
Parameters
Max. context
Size (GB)
Gemma4-31B-it [ 1 ]
31B
256k
62.6
InternVL3-8B [ 50 ]
8B
40k
15.9
Ovis-2.6-30B-A3B [ 27 ]
30B
64K
62.8
Qwen3-VL-8B-Instruct [ 3 ]
8B
256K
17.5
TABLE II: Open source VLMs used for this work.
Fig. 1: Diagram representing the models’ standalone FR test. Each model would get the 200,000 image pairs and be asked to output a similarity score between [0,1] and a justification for each of them.
Fig. 2: Diagram of the models’ fusion. Each model would produce a similarity score and justification for each image pair. This information will then be used part of the fusion. There are four scenarios: scenario A where the decider model will get the similarity scores of two source models, scenario B where the decider model will get the similarity scores and justification of two source models. For scenarios C and D there heterogeneous data-sources are used, scenario C where the decider model has access to the original face image pairs and the similarity scores of two source models, and scenario D where the decider model will also have access to the original face image pairs, in addition to the similarity scores and justifications.
TABLE VIII: Scenario D FR evaluation metrics for the 12 fusion combinations.
Fig. 9: Scenario D DET curves presented are grouped by decider model: (a) Gemma (b) Intern (c) Ovis (d) Qwen.
Fig. 10: Edge cases selected for analysing the quality of the explanations.
Fig. 11: Edge cases selected for analysing the quality of the explanations, (a) corresponds to scenario C where Ovis had access to only the scores produced by Intern and Gemma, and (b) corresponds to scenario D where Gemma has access to the scores and justifications from Quen and Intern.