Generation　of　realistic　face　images　by　age　synthesis　can　effectively　improve　the　accuracy　of　cross-age　face　verification,　which　is　of　great　significance　for　finding　the　missing　population.　However,　the　immature　skull　complex　of　adolescents　makes　it　very　difficult　to　age　synthesis.　Therefore,　an　end-to-end　age　synthesis　model　for　adolescents　is　proposed.　The　semantic　information　of　the　face　is　preserved　through　StyleGAN,　the　age　channel　is　added　to　the　face　coding　feature　to　realize　the　age　conversion,　the　affinity　feature　matching　module　is　introduced　to　guide　the　adolescents’facial　aging,　and　the　affinity　feature　matching　rate　is　added　to　the　loss　function　in　the　training.　The　algorithm　model　can　achieve　smooth　age　synthesis　and　generate　realistic　face　images　while　maintaining　individual　identity　information,　which　not　only　improves　the　visual　effect,　but　also　shows　that　the　accuracy　of　cross-age　face　verification　reaches　95.3%,　the　identity　recall　rate　reaches　92.7%,　and　the　average　age　error　of　age　synthesis　decreases　by　4　years,　which　is　better　than　the　existing　algorithms.　©　The　Author(s)　2023.