Color　is　one　of　the　most　stable　attributes　of　vehicles　and　often　used　as　a　valuable　cue　in　some　important　applications.　Various　complex　environmental　factors,　such　as　illumination,　weather,　noise　and　etc.,　result　in　the　visual　characteristics　of　the　vehicle　color　being　obvious　diversity.　Vehicle　color　recognition　in　complex　environments　has　been　a　challenging　task.　The　state-of-the-arts　methods　roughly　take　the　whole　image　for　color　recognition,　but　many　parts　of　the　images　such　as　car　windows;　wheels　and　background　contain　no　color　information,　which　will　have　negative　impact　on　the　recognition　accuracy.　In　this　paper,　a　novel　vehicle　color　recognition　method　using　local　vehicle-color　saliency　detection　and　dual-orientational　dimensionality　reduction　of　convolutional　neural　network　(CNN)　deep　features　has　been　proposed.　The　novelty　of　the　proposed　method　includes　two　parts:　(1)　a　local　vehicle-color　saliency　detection　method　has　been　proposed　to　determine　the　vehicle　color　region　of　the　vehicle　image　and　exclude　the　influence　of　non-color　regions　on　the　recognition　accuracy;　(2)　dual-orientational　dimensionality　reduction　strategy　has　been　designed　to　greatly　reduce　the　dimensionality　of　deep　features　that　are　learnt　from　CNN,　which　will　greatly　mitigate　the　storage　and　computational　burden　of　the　subsequent　processing,　while　improving　the　recognition　accuracy.　Furthermore,　linear　support　vector　machine　is　adopted　as　the　classifier　to　train　the　dimensionality　reduced　features　to　obtain　the　recognition　model.　The　experimental　results　on　public　dataset　demonstrate　that　the　proposed　method　can　achieve　superior　recognition　performance　over　the　state-of-the-arts　methods.　©　2017,　Springer　Science+Business　Media,　LLC.