The face provides a rich canvas of emotion. Humans are innately programmed to express and communicate emotion through facial expressions. Affdex scientifically measures and reports the emotions and facial expressions using sophisticated computer vision and machine learning techniques.
When you use the Affdex SDK in your applications, you will receive facial expression output in the form of Affdex metrics: seven emotion metrics, 20 facial expression metrics, 13 emojis, and four appearance metrics.
Furthermore, the SDK allows for measuring valence and engagement, as alternative metrics for measuring the emotional experience.
Engagement: A measure of facial muscle activation that illustrates the subject’s expressiveness. The range of values is from 0 to 100.
Valence: A measure of the positive or negative nature of the recorded person’s experience. The range of values is from -100 to 100.
The Emotion predictors use the observed facial expressions as input to calculate the likelihood of an emotion. See more information.
Emotion, Expression and Emoji metrics scores indicate when users show a specific emotion or expression (e.g., a smile) along with the degree of confidence. The metrics can be thought of as detectors: as the emotion or facial expression occurs and intensifies, the score rises from 0 (no expression) to 100 (expression fully present).
In addition, we also expose a composite emotional metric called valence which gives feedback on the overall experience. Valence values from 0 to 100 indicate a neutral to positive experience, while values from -100 to 0 indicate a negative to neutral experience.
Our SDKs also provide the following metrics about the physical appearance:
The age classifier attempts to estimate the age range.
Supported ranges: Under 18, from 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65 Plus.
The ethnicity classifier attempts to identify the person’s ethnicity.
Supported classes: Caucasian, Black African, South Asian, East Asian and Hispanic.
At the current level of accuracy, the ethnicity and age classifiers are more useful as a quantitative measure of demographics than to correctly identify the age and ethnicity on an individual basis. We are always looking to diversify the data sources included in training those metrics to improve their accuracy levels.
The gender classifier attempts to identify the human perception of gender expression.
In the case of video or live feeds, the Gender, Age and Ethnicity classifiers track a face for a window of time to build confidence in their decision. If the classifier is unable to reach a decision, the classifier value is reported as “Unknown”.
A confidence level of whether the subject in the image is wearing eyeglasses or sunglasses.
The SDKs include our latest face tracker which calculates the following metrics:
Facial Landmarks Estimation
The tracking of the cartesian coordinates for the facial landmarks.
Head Orientation Estimation
Estimation of the head position in a 3-D space in Euler angles (pitch, yaw, roll).
The distance between the two outer eye corners.