Driver　fatigue　is　a　significant　factor　in　many　traffic　accidents.　We　propose　a　novel　dynamic　features　using　feature-level　fusion　for　driver　fatigue　detection　from　facial　image　sequences.　First,　Gabor　filters　are　employed　to　extract　multi-scale　and　multi-orientation　features　from　each　image,　which　are　then　merged　according　to　a　fusion　rule　to　produce　a　single　feature.　To　account　for　the　temporal　aspect　of　human　fatigue,　the　fused　image　sequence　is　divided　into　dynamic　units,　and　a　histogram　of　each　dynamic　unit　is　computed　and　concatenated　as　dynamic　features.　Finally　a　statistical　learning　algorithm　is　applied　to　extract　the　most　discriminative　features　and　construct　a　strong　classifier　for　fatigue　detection.　The　test　data　contains　600　image　sequences　from　thirty　people.　Experimental　results　show　the　validity　of　the　proposed　approach,　and　the　correct　rate　is　much　better　than　the　baselines.