Although　robotic　grippers　have　been　extensively　used　in　industry　nowadays,　most　of　them　still　are　lack　of　tactile　perception　to　achieve　some　dexterous　manipulation　like　grasping　an　unknown　object　using　appropriate　force.　Hence,　to　make　the　grippers　gain　multiple　types　of　tactile　information,　we　combine　the　gripper　with　the　dual-modal　vision-based　tactile　sensor　in　our　experiment.　Different　from　existed　texture　recognition　experiments,　we　build　own　texture　dataset　included　12　kinds　of　samples　using　the　novel　tactile　transducer.　At　the　same　time,　we　compare　K-Nearest　Neighbor　(KNN)　with　Residual　Network　(ResNet),　the　experiment　results　showcase　that　the　accuracy　of　KNN,　is　only　66.11%,　while　the　accuracy　of　ResNet　based　on　deep　convolution　neural　network　is　as　high　as　100.00%.　In　addition,　to　detect　the　contact　force,　we　employ　the　nonlinear　characteristic　of　BP　neural　network　to　establish　the　mapping　relation　between　the　two-dimensional　displacement　image　of　markers　and　the　three-dimensional　(3D)　force　vector.　Experiments　are　implemented　to　demonstrate　the　sensor＇s　performance　of　predicting　the　force　within　4%　margin　of　error.　©　2021　IEEE.