Fatigue　driving　is　an　important　reason　of　traffic　accidents.　Yawning　is　an　evidence　of　driver　fatigue.　This　paper　proposes　to　locate　and　track　a　driver＇s　mouth　movement　using　a　CCD　camera　to　study　on　monitoring　and　recognizing　a　driver＇s　yawning.　Firstly　detecting　drivers＇　faces　uses　Gravity-Center　template,　then　detecting　drivers＇　left　and　right　mouth　corners　by　grey　projection,　and　extracting　texture　features　of　drivers＇　mouth　corners　(left　and　right)　using　Gabor　wavelets.　Finally　LIDA　is　applied　to　classify　feature　vectors　to　detect　yawning.　The　method　is　tested　on　400　images　from　twenty　videos.　In　contrast,　yawning　is　also　detected　by　the　ratio　of　mouth　height　and　width.　The　experiment　results　show　that　Gabor　coefficients　are　more　powerful　than　geometric　features　to　detect　yawning　and　the　average　recognition　rate　is　95%　which　has　more　than　20%　improvement.