Facial　Beauty　Prediction　(FBP)　is　subjective　and　varies　from　person　to　person,　which　makes　it　difficult　to　obtain　a　unified　and　objective　evaluation.　Previous　efforts　adopt　conventional　convolution　neural　networks　to　extract　local　facial　features　and　calculate　corresponding　facial　attractiveness　scores,　ignoring　the　global　facial　features.　To　address　this　issue,　we　propose　a　dynamic　convolution　vision　transformer　named　FBPFormer　which　aims　to　focus　on　both　local　facial　features　and　the　global　facial　information　of　the　human　face.　Specifically,　we　first　build　a　lightweight　convolution　network　to　produce　pseudo　facial　attribute　embedding.　To　inject　the　global　facial　information　into　the　transformer,　the　parameters　of　encoders　are　dynamically　generated　by　the　embedding　of　each　instance.　Therefore,　these　dynamic　encoders　can　fuse　and　further　fuse　local　facial　features　and　global　facial　information　while　encoding　query,　key,　and　value　vectors.　Furthermore,　we　design　an　instance-level　dynamic　exponential　loss　to　dynamically　adjust　the　optimization　objectives　of　the　model.　Extensive　experiments　show　our　method　achieves　competitive　performance,　demonstrating　its　effectiveness　in　the　FBP　task.