Multilabel convolution neural network for facial expression recognition and ordinal intensity estimation.

ABSTRACT: Facial Expression Recognition (FER) has gained considerable attention in affective computing due to its vast area of applications. Diverse approaches and methods have been considered for a robust FER in the field, but only a few works considered the intensity of emotion embedded in the expression. Even the available studies on expression intensity estimation successfully assigned a nominal/regression value or classified emotion in a range of intervals. Most of the available works on facial expression intensity estimation successfully present only the emotion intensity estimation. At the same time, others proposed methods that predict emotion and its intensity in different channels. These multiclass approaches and extensions do not conform to man heuristic manner of recognising emotion and

SUBMITTER: Ekundayo O 

PROVIDER: S-EPMC8641570 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature