Posted on Apr 3, 2018 | Rating
   
  

Real-time Facial Emotion Detection Software Component

The asset detects real-time emotion based on facial expression of humans. The asset functions in three manners: 1) using a single image file, 2) using a recorded video file, and 3) using a real-time webcam stream.

Short non-technical description: The asset will be useful for emotion-based game adaptation in both entertainment and applying digital games. For the player: to have emotionally tailored gameplay experiences; For the developer: to provide emotional assessment during playtesting.

Technical description: The asset uses the simple and low-cost device: standard webcam to detect facial expressions of the player. This asset uses emotion classification suggested by Ekman and Friesen (1978), which is widely used in psychological research and practice, comprising six basic emotions: happiness, sadness, surprise, fear, disgust, and anger. In addition, it includes the complement, which is the neutral emotion.

Detailed description: This asset captures facial expressions of humans and provides six basic emotions (happy, sad, surprise, fear, disgust, and anger) as well as the neutral emotion, accordingly. The asset functions in three manners: 1) using a single image file, 2) using a recorded video file, and 3) using a real-time webcam stream. The asset can be used in different video games and in different domains. Moreover, it can be used in serious games and in interactive games.

Date: Jan 1, 2017

Language: English

Access URL: https://github.com/rageappliedgame/EmotionDetectionAsset

Download: Real-time-Facial-Emotion-Detection-Software-Component.zip

Emotion

Real-time

Detection

Recognition

Facial expressions

Feedback

Human-Computer Interaction

Webcam



Game development environment: Unity

Target platform: Windows

Programming language: C#

Version: 1.0

Version notes: Alpha version, Uses DLIB for face detection and landmarks detection.

Development status: Completed

Commit URL: https://github.com/rageappliedgame/EmotionDetectionAsset

Type: Apache 2.0 (Apache License 2.0)

URL: https://opensource.org/licenses/Apache-2.0

Personal skills Effectiveness Flexibility Self-confidence Self-control and Stress Resistance Recognize objects Learn new tasks willingly and Lifelong learning Self-discipline Self-reflection
Computers in other domains Computer games
Education Distance learning E-learning Interactive learning environments
Life and medical sciences Health care information systems
Component Affective State Detection

Related Articles

Component
Real-Time Arousal Detection Using Galvanic Skin Response
Dessislava Vassileva, Boyan Bontchev, Rage project, Sofia University

Multimedia
Emotion Detection

Component
Adaptation and Assessment (TwoA) component
Enkhbold Nyamsuren, Rage project, Enkhbold Nyamsuren


Component
Physical Exercise Evaluation
PATHway Project, CERTH-ITI, CERTH-Information Technologies Institute, Rage project, CERTH-Information Technologies Institute

Component
Evaluation Component
TUGraz, CSS, Rage project, TUGraz, CSS


Component
Performance Statistics
Open University of the Netherlands, Rage project, Rage project

Component
Client Side Game Storage Asset
Rage project, Rage project, Open University of the Netherlands

Component
Competence Assessment Component
TU Graz, Rage project, TU Graz, CSS

×