To　improve　driving　safety,　the　authors　propose　an　approach　to　locate　a　driver＇s　mouth　by　a　web　camera　and　extract　texture　features　from　mouth　corners　for　monitoring　drivers＇　yawning.　Firstly,　it　detects　drivers＇　left　and　right　mouth　corners　by　gray　projection　based　on　the　result　of　driver　face　detection,　and　then　it　extracts　texture　features　of　drivers＇　mouth　corners　by　Gabor　wavelets.　Finally,　LDA　is　used　to　classify　Gabor　features　for　yawning　detection.　The　proposed　approach　is　tested　on　3000　images　from　thirty　subjects　with　variations　in　illuminations,　poses,　and　facial　accessories　(glasses).　Yawning　is　also　detected　by　the　ratio　of　mouth　height　to　width　as　a　baseline.　Experiment　results　show　that　the　proposed　approach　is　suitable　for　real　time　yawning　detection,　Gabor　features　are　more　powerful　than　geometric　features　for　yawning　representation,　and　an　average　recognition　rate　of　91.97%　is　achieved　which　is　much　better　than　the　baseline.