A5037525913- Emotion and Mood Recognition
- Face and Expression Recognition
- Face recognition and analysis
- Gaze Tracking and Assistive Technology
- Advanced Chemical Sensor Technologies
- Deception detection and forensic psychology
Sudan University of Science and Technology
2018-2020
Micro-expressions are brief spontaneous facial expressions that appear on a face when person conceals an emotion, making them different to normal in subtlety and duration. Currently, emotion classes within the CASME II dataset (Chinese Academy of Sciences Micro-expression II) based Action Units self-reports, creating conflicts during machine learning training. We will show classifying using Units, instead predicted removes potential bias human reporting. The proposed tested LBP-TOP (Local...
Micro-facial expressions are regarded as an important human behavioural event that can highlight emotional deception. Spotting these movements is difficult for humans and machines, however research into using computer vision to detect subtle facial growing in popularity. This paper proposes individualised baseline micro-movement detection method 3D Histogram of Oriented Gradients (3D HOG) temporal difference method. We define a face template consisting 26 regions based on the Facial Action...
This paper presents baseline results for the first Facial Micro-expressions Grand Challenge (MEGC) 2018 by evaluating LBP-TOP, HOOF and 3DHOG on CASME II SAMM. We further improve result of composite database evaluation (Task B challenge) introducing selective block-based features fusion representation. Base objective classes, this task combines SAMM into a single uses Leave-One-Subject-Out crossvalidation to evaluate performance. Our proposed method achieve F1-Score 0.579, which outperformed...
Facial micro-expression can be characterized by its short duration and subtle movements. In facial recognition, these movements require more specific feature descriptors due to only a few parts of the face produce information that helps us recognize micro-expressions. Over past decade, researchers designed different Region Interests (ROIs) study regions in micro-expressions recognition. To further this aspect, we proposed region-based method with an adaptive mask for Based on most frequent...
Facial micro-expression datasets lack consistency and standardisation, with different research groups using various experimental settings, in particular, where the are varied resolution frame rates. To provide new insights into roles of rate resolution, we conduct an investigation use rates on current benchmark (SMIC CASME II). By Temporal Interpolation Model, subsample SMIC (original is 100 fps) to 50 fps II 200 fps. In addition, settings adjusted three scaling factors: 100% resolution),...
Micro-expressions are brief spontaneous facial expressions that appear on a face when person conceals an emotion, making them different to normal in subtlety and duration. Currently, emotion classes within the CASME II dataset based Action Units self-reports, creating conflicts during machine learning training. We will show classifying using Units, instead of predicted removes potential bias human reporting. The proposed tested LBP-TOP, HOOF HOG 3D feature descriptors. experiments evaluated...