Ensemble CCA for continuous emotion prediction

This paper presents our work on ACM MM Audio Visual Emotion Corpus 2014 (AVEC 2014) using the baseline features in accordance with the challenge protocol. For prediction, we use Canonical Correlation Analysis (CCA) in affect sub-challenge (ASC) and Moore-Penrose generalized inverse (MPGI) in depression sub-challenge (DSC). The video baseline provides histograms of Local Gabor Binary Patterns from Three Orthogonal Planes (LGBP-TOP) features. Based on our preliminary experiments on AVEC 2013 challenge data, we focus on the inner facial regions that correspond to eyes and mouth area. We obtain an ensemble of regional linear regressors via CCA andMPGI.We also enrich the 2014 baseline set with Local Phase Quantization (LPQ) features extracted using Intraface toolkit detected/tracked faces. Combining both representations in a CCA ensemble approach, on the challenge test set we reach an average Pearson's Correlation Coefficient (PCC) of 0.3932, outperforming the ASC test set baseline PCC of 0.1966. On the DSC, combining modality specific MPGI based ensemble systems, we reach 9.61 Root Mean Square Error (RMSE).