The path to human insights
FaceReader Online brings the advanced analysis capabilities of the FaceReader ecosystem to the cloud, leveraging data-driven computer vision and machine learning models with scientific research methodologies anchored in emotion theory and human behavioral research.
Neuromarketing
Neuromarketing tools help you develop a deep understanding of how your audience responds to your creative content. Neuromarketing sits at the intersection of marketing, psychology, and neuroscience, providing insights into what drives decisions. While neuroscience techniques like EEG and fMRI are powerful, neuromarketing tools can also be used remotely. Online neuromarketing tools do not require dedicated, expensive hardware, which makes them affordable and scalable. By analyzing behavior patterns through facial coding and eye tracking, researchers gain valuable insights into both conscious and subconscious consumer responses.
Facial coding
The facial coding software within FaceReader Online helps understand real human reactions in the moment they are experienced. Facial coding is an AI technology that quantifies basic emotional expressions and Action Units (AUs) by using the Facial Action Coding System (FACS). This type of direct implicit nonverbal behavior is an important addition to retrospective explicit survey responses. The granular analysis of facial behavior allows for an unbiased, precise representation of reactions, transforming subtle changes into quantifiable data. These insights have a robust scientific foundation and can be used to promote emotional storytelling in advertising and make fluent UX applications.
Eye tracking
Affective computing
At the heart of the FaceReader ecosystem lies Affective computing – the intersection of artificial intelligence, psychology, and cognitive science – that enables machines to measure human emotions and behavior. In FaceReader, this is the scientific foundation that powers its facial analysis and interpretation. Through joint analysis of facial cues, gestures, expressions, and eye movement patterns, FaceReader Online can translate complex emotional signals into actionable insights, embodying the cutting-edge of this empathic technology.
Basic emotions and theories of emotion
It builds upon these concepts of basic emotions and FACS action units to operationalize Russell’s Valence/Arousal model. FaceReader Online maps key emotional signals from expressions and AUs onto the circumplex dimensions of Valence (pleasant-unpleasant) and Arousal (active-inactive), thereby providing a rich multi-dimensional understanding of affective states.
In addition, it also offers flexibility to accommodate alternate theories of emotion, enabling users to apply the software within diverse theoretical frameworks and research contexts, thereby broadening its applicability and relevance across various fields of study.
Intersection of Emotional Response and Gaze Tracking
Eye tracking provides objective data on where and how long individuals look at specific elements, while emotional response analysis deciphers the affective impact of what they see. This synergy allows researchers to not only ascertain which aspects of a stimulus draw attention but also understand the emotional reactions they evoke.
With FaceReader Online, this dual analysis facilitates deeper insights into user engagement, effectiveness of content, and consumer preferences. The integration of these two powerful tools is not just a technical achievement but also a reflection of an established research paradigm that emphasizes the importance of a multi-modal approach to understanding human emotions and behaviors, providing researchers with a sophisticated, evidence-based framework to inform their studies and strategies.
Validated core AI models
Creates a 3D model of the face and detects over 500 keypoints
Models the eyes and derives gaze angles
Advanced algorithm to calculate the heart rate form changes in redness
Advanced algorithm to calculate the breathing rate
Classifies over 20 facial action units
Classifies Gender, Age and the presence of Glasses.
Classifies the 6 basic universal emotions
Facial analysis in FaceReader Online is supplemented by its gaze tracking algorithm. This deep learning-based model is able to compute the direction of the users’ gaze using only webcam images, and has been validated to produce results within an error of ~5°. By hybridizing 3D geometry and machine learning based calibration procedures, this gaze-tracking model can determine where the user is looking at on the screen with an error of ~2.5cm – which is typically sufficient to resolve banners and buttons on website – with minimal calibration effort. Furthermore, the pattern of eye movements is also analyzed to recognize movement patterns and distinguish between fixations and saccades, which are indicative of attention and cognitive load.
Trained on high quality data
