Facetracking – Biometric Research Using the Affectiva Algorithm
Facetracking is an advanced biometric technology that enables the analysis of emotions based on facial expressions. By using a webcam, it allows precise monitoring of respondents' emotional reactions both in laboratory settings and in online research. At the Laboratory of Media Studies UW, we use the advanced Affectiva algorithm for facetracking, integrated with the iMotions platform.
Affectiva Algorithm – A Global Standard for Emotion Analysis
Affectiva is one of the most advanced tools for emotion analysis, recognized as a global standard in scientific, marketing, and psychological research worldwide. This technology is used in a variety of fields, from advertising and UX research to psychology and neuromarketing. Affectiva is based on artificial intelligence and machine learning, allowing it to precisely detect micro-movements of the face and identify emotions in real-time.
Scientific Foundations – Research by Paul Ekman
Affectiva is based on research conducted by Paul Ekman, one of the pioneers in the field of psychology of emotions and facial expressions. By analyzing universal facial expression patterns, Ekman discovered that certain emotions are expressed similarly across different cultures. Based on this, he developed the Facial Action Coding System (FACS), which became the foundation for algorithms like Affectiva.
In the 1970s, Ekman conducted research in various parts of the world, including among indigenous tribes in Papua New Guinea, proving that emotions such as joy, sadness, fear, and anger have universal facial expression patterns. His work laid the foundation for modern facetracking technologies, which are widely used today.
More about Paul Ekman’s research can be found in the interview "Conversations with History: Paul Ekman", where he discusses his studies on human facial expressions and emotions, as well as their significance for interpersonal communication and self-understanding. Watch the interview here: Conversations with History: Paul Ekman.
What Emotions Does Affectiva Analyze?
Affectiva identifies seven primary emotions and other facial expressions that provide additional insights into the participant’s emotional state. The key emotions analyzed by the algorithm include:
- Joy
- Sadness
- Anger
- Fear
- Disgust
- Surprise
- Neutral
Additionally, Affectiva monitors indicators such as attention levels, emotional engagement, and facial expressions (e.g., smiling, furrowing brows).
Facetracking in the Laboratory and Online
Thanks to Affectiva’s technology, facetracking research can be conducted both in controlled laboratory conditions and remotely, online. In laboratory settings, high-resolution cameras and the iMotions platform are used to integrate biometric facetracking data with other measures, such as eye tracking and galvanic skin response (GSR).
In online studies, a standard webcam is sufficient, making this method accessible to a broad range of respondents and allowing data collection from various geographical locations.
Facetracking as Biometric Research
Facetracking falls under the category of biometric research, as it utilizes physiological responses (facial expressions) to analyze emotional states. This is an extremely valuable tool in scientific, marketing, and social research, as it provides objective data on emotions that are often difficult to capture in traditional self-reported studies.
Through triangulation of methods, which involves combining multiple measurement techniques, facetracking can be integrated with other biometric data, such as EEG, eye tracking, or GSR. This enables a more comprehensive analysis of participants' reactions, offering a fuller picture of their emotional states.
Conclusion
Facetracking using the Affectiva algorithm is a modern and effective method of emotion analysis, based on psychological research and artificial intelligence. The ability to conduct studies both in the laboratory and online makes this technology highly versatile. At the Laboratory of Media Studies UW, we use it to better understand respondents' emotions and behaviors, enabling innovative and precise research.
