Visible-infrared　person　re-identification　(VI-ReID)　task　aims　to　retrieve　persons　from　different　spectrum　cameras　(i.e.,　visible　and　infrared　images).　The　biggest　challenge　of　VI-ReID　is　the　huge　cross-modal　discrepancy　caused　by　different　imaging　mechanisms.　Many　VI-ReID　methods　have　been　proposed　by　embedding　different　modal　person　images　into　a　shared　feature　space　to　narrow　the　cross-modal　discrepancy.　However,　these　methods　ignore　the　purification　of　identity　features,　which　results　in　identity　features　containing　different　modal　information　and　failing　to　align　well.　In　this　article,　an　identity　feature　disentanglement　method　is　proposed　to　disentangle　the　identity　features　from　identity-irrelevant　information,　such　as　pose　and　modality.　Specifically,　images　of　different　modalities　are　first　processed　to　extract　shared　features　that　reduce　the　cross-modal　discrepancy　preliminarily.　Then　the　extracted　feature　of　each　image　is　disentangled　into　a　latent　identity　variable　and　an　identity-irrelevant　variable.　In　order　to　enforce　the　latent　identity　variable　to　contain　as　much　identity　information　as　possible　and　as　little　identity-irrelevant　information,　an　ID-discriminative　loss　and　an　ID-swapping　reconstruction　process　are　additionally　designed.　Extensive　quantitative　and　qualitative　experiments　on　two　popular　public　VI-ReID　datasets,　RegDB　and　SYSU-MM01,　demonstrate　the　efficacy　and　superiority　of　the　proposed　method.