ROUTE, FUSE, AND VERIFY: COUNTERFACTUAL EVIDENCE-GUIDEDEXPERTROUTINGFORWHOLE BODYHUMANRECOGNITION

Multimodal human recognition can exploit complementary face, gait, and body-appearance experts, but existing methods typically adapt only one part of the recognition pipeline, such as model weighting, expert selection, or score calibration. Because these stages rely on separate signals, the system lacks a candidate-specific representation indicating whether the selected models provide sufficient identity evidence and whether the query may come from outside the gallery. As a result, they lack a direct way to determine whether the current evidence is sufficient for an identity decision or whether an additional expert is likely to provide useful identity information. We propose a query-adaptive framework that connects model routing, score fusion, and open-set recognition through calibrated identity evidence. For each selected model subset, the Calibrated Identity Evidence Model (CIEM) estimates candidate genuineness and query unknownness, using their difference as a verification certificate. The certificate guides a multimodal policy optimized with GRPO to select an anchor expert, invoke complementary experts, or stop inference. Its routing reward combines retrieval utility, verification and open-set safety, decision correctness, and the cost of additional expert calls. Given the selected route, Anchor-Preserving Temporal-Consensus (APTC) uses the anchor's complete score vector as the global base and adds high-confidence temporal evidence from complementary experts. Certificate-Reanchored Identity Scoring (CRIS) then projects CIEM certificate margins onto the APTC identity ranking through an order-preserving isotonic transformation. Experiments on CCVID, MEVID, and LTCC show that the resulting pipeline improves retrieval, verification, and open-set identification over fixed fusion, quality-guided fusion, and metric-reward routing baselines.