Recently,　in　a　task　of　face　recognition,　some　researchers　proposed　a　novel　method　called　color　space　normalization　(CSN)　which　can　enhance　the　discriminating　power　of　color　spaces　for　color　face　recognition.　This　method　can　significantly　reduce　the　correlation　between　the　three　color　components　RGB　and　thus　can　enhance　the　discriminating　power　of　the　concatenated　color　component　RGB.　This　offers　an　intrinsic　(or　internal)　reason　for　why　the　CSN　techniques　can　improve　the　face　recognition　performance.　The　three　color　component　images　of　normalized　color　spaces　are　concatenated　into　one　pattern　vector,　then　PCA+FLD　is　performed　on　the　concatenated　pattern　vector.　But　the　concatenated　pattern　vector　based　on　PCA　destroyed　the　overall　structure　of　each　pixel　in　image.　Some　local　structure　information　may　be　ignored.　While　2DPCA　preserves　the　overall　structure　information　well　and　eliminates　the　small　sample　problem　on　a　certain　extent.　Therefore,　in　this　paper,　color　space　normalization　was　combined　with　2DPCA　for　color　face　recognition.　This　CSN　based　2DPCA　was　performed　and　compared　with　PCA,　2DPCA　for　color　face　recognition.　Numerous　experiments　show　that　the　proposed　approach　has　better　recognition　performance　than　other　traditional　PCA-based　methods　for　color　face　recognition.　©2010　IEEE.