With　the　development　of　video　and　image　technology,　it　is　of　great　practical　value　to　recognize　specific　persons　in　television　programs　and　photo　albums.　However,　occlusion　of　body　parts　and　changes　in　shooting　position　and　distance　are　common　in　real　scenes.　In　this　work,　we　proposed　a　Specific　Person　Recognition　based　on　Local　Segmentation　and　Fusion　method,　called　PR-LSF,　which　improved　the　reliability　of　person　recognition　in　these　environments.　We　represented　the　human　body　as　an　aggregate　of　multiple　parts　and　apply　local　segmentation　to　train　multiple　convolutional　neural　network　(CNN)　classifiers.　Each　part　classifier　generated　an　identification　decision　confidence　for　each　part.　By　training　the　SVM　classifier,　we　weighted　the　decision　confidence　of　all　parts　to　make　a　comprehensive　judgment.　To　verify　the　effectiveness　of　the　proposed　algorithm,　we　performed　experiments　with　unoccluded　and　occluded　test　sets.　The　experimental　results　demonstrated　that　PR-LSF　achieved　higher　recognition　performance　than　algorithms　using　a　single　body　part　and　were　reliable　even　with　partial　occlusions,　multiple　scenes,　and　shooting　changes.　©　2019　IEEE.