Gender and Emotion Recognition from Implicit User Behavior Signals

TL;DR

This study demonstrates that low-cost EEG and eye movement signals can reliably identify gender and emotions, revealing gender-specific cognitive processing and eye gaze differences under face occlusion.

Contribution

It introduces the use of affordable sensors for implicit behavioral cues to recognize gender and emotions from facial interactions.

Findings

EEG signals show gender-specific differences.

Eye movements correlate with perceived emotions.

Gender differences in eye gaze are evident under face occlusion.

Abstract

This work explores the utility of implicit behavioral cues, namely, Electroencephalogram (EEG) signals and eye movements for gender recognition (GR) and emotion recognition (ER) from psychophysical behavior. Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. 28 users (14 male) recognized emotions from unoccluded (no mask) and partially occluded (eye or mouth masked) emotive faces; their EEG responses contained gender-specific differences, while their eye movements were characteristic of the perceived facial emotions. Experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing of negative emotions is observed for females and (c) eye gaze-based gender differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.