An Integrated Design of Face and Facial Expression Recognition

• Main Authors: Chen, Yi-Wen, 陳奕彣
• Other Authors: Song, Kai-Tai
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/30385550048722263484

碩士 === 國立交通大學 === 電控工程研究所 === 98 === In this thesis, an integrated design of face and facial expression recognition system has been developed for robotic applications. First, facial image from camera is exacted to compute facial shape and texture model using active appearance model (AAM). Second, we use modified Lucas-Kanade image alignment algorithm to find facial features. Third, the texture model of AAM is used to construct facial texture parameters. These parameters are used to train a back propagation neural network (BPNN) for face and facial expression recognition. In recognition process, we first use face recognition to find user’s identity; then we use recognized user’s facial expression database to recognize his/her facial expression. In experiments based on BU-3DFE database, a face recognition rate of 98.3% has been achieved. The facial expression recognition rate of the proposed integrated method (using a personal facial expression classifier) is 83.8%. It is a great improvement compared with using conventional facial expression classifier of 69.6%.