Analysis and compression of facial animation parameter set (FAPs)

Author

Tao, Hai ; Chen, Homer ; Huang, Thomas

Author_Institution

Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA

fYear

1997

fDate

23-25 Jun 1997

Firstpage

245

Lastpage

250

Abstract

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

Keywords

computational complexity; computer animation; data compression; data structures; face recognition; image coding; recurrent neural nets; computational complexity; data representation; facial animation parameter set; facial expression recognition algorithm; principal component analysis; recurrent neural network; sampling rate; Computational complexity; Covariance matrix; Face recognition; Facial animation; Financial advantage program; Hidden Markov models; Image sampling; Principal component analysis; Recurrent neural networks; Rendering (computer graphics);

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia Signal Processing, 1997., IEEE First Workshop on

Conference_Location

Princeton, NJ

Print_ISBN

0-7803-3780-8

Type

conf

DOI

10.1109/MMSP.1997.602643

Filename

602643