Variable-state Latent Conditional Random Field models for facial expression analysis

Variable-state Latent Conditional Random Field models for facial expression analysis

Automated recognition of facial expressions of emotions, and detection of facial action units (AUs) from videos depends critically on modeling of their dynamics. Some of these dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion expressions and AUs. The appearance of these changes may vary considerably among subjects, making the recognition/detec...

Facial Expression Recognition Using Sparse Gaussian Conditional Random Field

The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works has been done during the past few years which has their own advantages and disadvantages. In this work we present a new model based on Gaussian Conditional Random Field. We so...

Predictive Random Fields: Latent Variable Models Fit by Multiway Conditional Probability with Applications to Document Analysis

We introduce predictive random fields, a framework for learning undirected graphical models based not on joint, generative likelihood, or on conditional likelihood, but based on a product of several conditional likelihoods each relying on common sets of parameters and predicting different subsets of variables conditioned on other subsets. When applied to models with latent variables, such as th...

Latent Variable Models for Hippocampal Sequence Analysis

VIRTUAL TUNING CURVES we only train the HMMs on spikes from PBEs; to determine if the inferred states encode position data, we compute virtual tuning curves in two ways: (A) by decoding RUN data using the PBE-only HMM, and then using the true position data to estimate a map from states to position, and (B) by using the Bayesian decoder to estimate position during PBEs, and to learn a map from t...

Multivariate latent Gaussian random field mixture models