Evaluation of spatio-temporal regional features For 3D face analysis
Citations Over Time
Abstract
3D facial representations have been widely used for face recognition and facial expression recognition. Both local and global features can be extracted from either static or dynamic models in both spatial and temporal domains. 3D local features are referred to the features in regional facial areas while 3D global features are referred to the features from the entire facial region. In this paper, we address the issue of performance assessment of facial analysis in terms of global features versus local features, static models versus dynamic models, and spatial domain versus temporal domain. Based on the existing work of using 3D spatio-temporal HMM for facial analysis, we propose to extend it to a local-temporal HMM in order to provide an explicit comparison of global features and local features. A dynamic 3D facial expression database and a static facial expression database are used for experiments. The performance for six prototypic facial expression classification and face identification is analyzed and reported.
Related Papers
- → Mid-long Term Load Forecasting Using Hidden Markov Model(2009)11 cited
- → On modeling context-dependent clustered states: Comparing HMM/GMM, hybrid HMM/ANN and KL-HMM approaches(2014)25 cited
- → A mixed autoregressive hidden-markov-chain model applied to people's movements(2012)12 cited
- → The Research of Software Behavior Recognition and Trend Prediction Method Based on GA-HMM(2015)
- Research of HMM and I/O HMM used in protein secondary structure prediction(2002)