A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

Abstract

Zhan YZ, Cheng KY, Chen YB et al. A new classifier for facial expression recognition: Fuzzy buried Markov model. Abstract To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model (FBMM) is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model (HMM) by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model (BMM), FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information (MMI) method to replace maximum likelihood (ML) method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.

Design and implementation of a virtual teacher teaching system algorithm based on facial expression recognition in the era of big data

“…In the literature [23][24][25], researchers have done much work on the research of virtual expression technology in recent decades and achieved certain research results, and he pointed out that the methods of driving virtual characters to generate expressions like real people are roughly divided into three categories: text-based methods, speech-based methods, and face expression capture-based methods. Among them, text-based and speech-based driving methods are mainly used to drive the mouth patterns of virtual characters, with the disadvantage that they are not sufficient to generate facial expression animations with a realistic sense [26][27][28]. The literature [29][30][31] argues that face expression capture-based methods mainly use special devices such as monocular and head-mounted cameras to capture face expression motion parameters to drive virtual character models, which can generate more realistic facial expression animations.…”