Communication in any form either verbal or non-verbal is vital to complete various daily routine tasks and plays a significant role in life. Facial expression is the most effective form of non-verbal communication and it provides a clue about emotional state

mindset and intention. Till now

facial expression recognition has been successfully applied to various fields such as safe driving

merchandise sales

clinical medicine

and so on. This thesis explores key techniques related to facial expression recognition. The main work and contributions are as follows. A dynamic facial expression recognition algorithm based on multi-visual descriptors and audio features is proposed under unrestricted conditions

in which dynamic facial feature extraction was conducted based on local spatial-temporal feature representation via multi-visual descriptors.Furthermore

the combination of video and audio features improves the recognition performance. Dynamic time warping based on timeline segmentation and covariance matrix proves to be effective in analyzing dynamic expression sequences of different time duration. To improve the generalization performance of facial expression recognition model

an integrated decision-making strategy based on weight voting by multiple individual recognition models is introduced. In order to effectively learning the weight for each individual recognition model

the method of voting weight learning by random re-sampling and the method of voting learning based on comparative advantages of individual recognition model are proposed. Finally the above ensemble model is applied and the recognition performance is further improved. Experiments on AFEW5.0 dataset validate the performance of the proposed dynamic facial expression algorithm.