Visible　light　positioning　(VLP)　is　a　low-cost,　highly　accurate　alternative　localization　technology　for　indoor　applications　that　makes　use　of　existing　light　emitting　diode　(LED)-based　lights,　which　is　highly　accurate　and　low　costs.　It　is,　however,　a　major　challenge　for　the　existing　VLP　systems　to　achieve　line-of-sight　(LOS)　positioning　in　complex　and　variable　indoor　environments.　We　propose　a　non-line-of-sight　(NLOS)　fingerprint-based　VLP　system　based　on　a　multi-pixel　photon　counter　(MPPC)　to　address　the　problem　of　obstructed　LOS　paths.　Using　MPPC,　very　faint　light　can　be　detected　with　a　very　high　sensitivity　and　excellent　photon　counting　capability,　which　enhances　the　ability　to　recognize　and　detect　signals　in　an　NLOS　environment.　We　propose　a　novel　method　of　generating　fingerprint　database　using　the　NLOS　channel　model,　which　construct　the　relationship　between　the　received　signal　strength　and　the　distance　from　MPPC　to　the　virtual　image　of　LED　interpolated　by　only　knowing　the　distance　between　the　LED　and　the　interpolation　position.　Furthermore,　we　propose　an　optimal　parameter　weighted　K-nearest　neighbor　algorithm,　which　utilizes　the　mean　absolute　error　(MAE)　as　the　evaluation　metric.　In　this　algorithm,　a　grid　search　method　is　employed　to　determine　the　optimal　number　of　neighbors　and　the　distance　metric　for　each　test　point,　thereby　enhancing　the　positioning　accuracy.　Using　only　25　offline　measurements,　the　measured　average　positioning　error　(PE)　and　90th　percentile　error　are　4.02　and　9.98　cm,　respectively,　when　the　MPPC　height　is　70　cm.　　©　2014　IEEE.