Device-free　activity　recognition　is　appealing　in　smart　home　applications.　It　not　only　is　convenient,　but　also　causes　no　privacy　concern,　as　compared　to　other　activity　recognition　techniques　such　as　the　vision　based　technique.　Existing　WiFi-based　methods　have　achieved　high　accuracy　in　static　circumstances　but　have　limitations　in　adapting　changes　in　environment　and　activities　locations.　In　this　paper,　we　propose　a　fine-grained　adaptive　location-independent　activity　recognition　system　(FALAR)　which　leverages　WiFi　signals　to　characterize　and　recognize　common　activities　regardless　of　inconsistency　of　mutative　surroundings.　FALAR　applies　fine-grained　channel　state　information　(CSI)　to　achieve　accurate　recognitions.　To　address　the　issue　of　environmental　changes,　we　present　a　Kernel　Density　Estimation　(KDE)　based　motion　extraction　method　and　a　coarse-to-fine　search　strategy　for　speedy　processing.　After　a　denoising　scheme,　we　introduce　Class　Estimated　Basis　Space　Singular　Value　Decomposition　(CSVD)　to　efface　the　static　path　in　the　background,　and　use　nonnegative　matrix　factorization　to　distinguish　various　activities　by　looking　into　the　signal　profiles.　We　evaluate　FALAR　using　two　commodity　WiFi　routers　in　a　typical　office　environment.　Our　results　show　that　it　achieves　remarkable　performance.　©　2018　IEEE.