Face　verification,　as　a　key　part　of　person　intelligence　tracing,　aims　to　determine　whether　the　same　person　is　in　two　face　photos,　which　can　be　approximately　transformed　into　a　binary　classification　problem.　In　this　paper,　the　siamese　neural　network　architecture　is　adopted　as　the　overall　structure　of　the　method,　and　the　traditional　bacbone　network　generally　uses　a　convolutional　neural　network,　this　paper　will　use　Vision　Transformer　as　the　backbone　network　as　the　method.　Compared　with　convolutional　neural　network,　its　multi-headed　attention　mechanism　can　better　focus　on　the　larger　sensory　field　and　extract　face　features,　which　can　then　better　perform　face　verification,　while　Binary　cross-entropy　is　used　as　the　loss　function.　After　the　validation　of　the　private　dataset,　the　model　can　recognize　faces　well　and　has　excellent　validation　effect,　which　is　of　great　value　for　the　field　of　human　intelligence　tracing.　©　2023　IEEE.