Analysis and compression of facial animation parameter set (FAPs)
In this paper, a new representation of FAPs based on principal component analysis is proposed. Based on this compact representation, a FAPs compression scheme is designed. A facial expression recognition algorithm using recurrent neural network is also investigated. The inputs to the network are the most significant components of this new data representation. Experimental results show that computational complexity is reduced and expressions can be correctly recognized even with changed sampling rate.