An integrated face and facial expression recognition system has been designed and tested for robotic applications. Facial images from a web camera are first acquired for facial shape and texture model generation using active appearance model (AAM). Modified Lucas-Kanade image alignment algorithm was adopted to find facial features as well as the texture model of AAM to construct facial texture parameters. These parameters are used to train back propagation neural networks (BPNN) for face and facial expression recognition. A novel design is proposed for an integrated facial expression recognition system. In the first stage, face recognition is performed to find user's identity; then the facial-expression database of the recognized user is employed to recognize his/her facial expressions. Experimental result based on BU-3DFE database show that a face recognition rate of 98.3% is achieved. The facial expression recognition rate of the proposed integrated method (using personal facial expression classifiers) is 83.8%, an improvement compared with 69.6% of using conventional classifiers.