Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection

11/09/2022
by   Krist Shingjergji, et al.
0

Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portrays a particular basic emotion. Every round played by the player creates new data that consists of a set of facial features and landmarks, already annotated with the emotion label of the target facial expression. Such an approach effectively creates a robust, sustainable, and continuous machine learning training process. We evaluated Facegame with an experiment that revealed several contributions to the field of affective computing. First, the gamified data collection approach allowed us to access a rich variation of facial expressions of each basic emotion due to the natural variations in the players' facial expressions and their expressive abilities. We report improved accuracy when the collected data were used to enrich well-known in-the-wild facial emotion datasets and consecutively used for training facial emotion recognition models. Second, the natural language prescription method used by the Facegame constitutes a novel approach for interpretable explainability that can be applied to any facial emotion recognition model. Finally, we observed significant improvements in the facial emotion perception and expression skills of the players through repeated game play.

READ FULL TEXT

page 1

page 3

page 4

page 6

page 7

research
03/30/2016

Exploiting Facial Landmarks for Emotion Recognition in the Wild

In this paper, we describe an entry to the third Emotion Recognition in ...
research
12/05/2022

A comparative study of emotion recognition methods using facial expressions

Understanding the facial expressions of our interlocutor is important to...
research
07/29/2020

The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems

The paper describes BIRAFFE2 data set, which is a result of an affective...
research
07/10/2016

Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework

In order to create an "in-the-wild" dataset of facial emotions with larg...
research
01/19/2022

The Role of Facial Expressions and Emotion in ASL

There is little prior work on quantifying the relationships between faci...
research
08/05/2022

A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices

Facial emotional recognition is one of the essential tools used by recog...
research
12/27/2015

Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation,...

Please sign up or login with your details

Forgot password? Click here to reset