Spatiotemporal Features and Local Relationship Learning for Facial Action Unit Intensity Regression

Abstract

The action units (AU) encoded by the Facial Action Coding System (FACS) have been widely used in the representation of facial expressions. Although work on automatic facial AU detection has achieved quite good results in recent years, there remains much research potential for more accurate AU detection and intensity regression. Moreover, most work only considers the spatial information and ignores the temporal information. In practice, changes in facial AUs involve both spatial and temporal variation. In this paper, by extracting multi-scale spatial features and corresponding temporal features from the faces in the video image sequence, and learning the local relationship of the spatiotemporal features we propose a method that can obtain robust and accurate regression for AU intensity. The proposed method outperforms the baseline system on FEAFA dataset and obtains comparable performance on DISFA dataset.

Descriptive Analysis of Human Emotions Based on Eye pupils

“…[11] both databases were shaped based on deliberate expressions, whereas D.I.S.F.A. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] was created by using unprompted expressions. Accordingly, well-built datasets are prominent due to their ability to include as many aspects as possible.…”

“…Eye-Tracking is said to be the method of identification of the gaze point or the spot where users are watching for a specific visual stimulus. Different scholars use different devices for Eye-tracking, like Virtual Reality [16,17,28], Mobile Eye-tracking [14][15], and Desktop Eye-tracking [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]…”