Visual Emotion Recognition Through Multimodal Cyclic-Label Dequantized Gaussian Process Latent Variable Model

Abstract

A multimodal cyclic-label dequantized Gaussian process latent variable model (mCDGP) for visual emotion recognition is presented in this paper. Although the emotion is followed by various emotion models that describe cyclic interactions between them, they should be represented as precise labels respecting the emotions’ continuity. Traditional feature integration approaches, however, are incapable of reflecting circular structures to the common latent space. To address this issue, mCDGP uses the common latent space and the cyclic-label dequantization by maximizing the probability function utilizing the cyclic-label feature as one of the observed features. The likelihood maximization problem provides limits to preserve the emotions’ circular structures. Then mCDGP increases the number of dimensions of the common latent space by translating the rough label to the detailed one by label dequantization, with a focus on emotion continuity. Furthermore, label dequantization improves the ability to express label features by retaining circular structures, making accurate visual emotion recognition possible. The main contribution of this paper is the implementation of feature integration through the use of cyclic-label dequantization.