In　facial　expression　recognition　tasks,　different　facial　expressions　are　often　confused　with　each　other.　Motivated　by　the　fact　that　a　learned　metric　can　significantly　improve　the　accuracy　of　classification,　a　facial　expression　recognition　algorithm　based　on　local　metric　learning　is　proposed.　First,　k-nearest　neighbors　of　the　given　testing　sample　are　determined　from　the　total　training　data.　Second,　chunklets　are　selected　from　the　k-nearest　neighbors.　Finally,　the　optimal　transformation　matrix　is　computed　by　maximizing　the　total　variance　between　different　chunklets　and　minimizing　the　total　variance　of　instances　in　the　same　chunklet.　The　proposed　algorithm　can　find　the　suitable　distance　metric　for　every　testing　sample　and　improve　the　performance　on　facial　expression　recognition.　Furthermore,　the　proposed　algorithm　can　be　used　for　vector-based　and　matrix-based　facial　expression　recognition.　Experimental　results　demonstrate　that　the　proposed　algorithm　could　achieve　higher　recognition　rates　and　be　more　robust　than　baseline　algorithms　on　the　JAFFE,　CK,　and　RaFD　databases.　(C)　2016　SPIE　and　IS&T