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.

How do we map facial expressions to emotions?

The Emotion predictors use the observed facial expressions as input to calculate the likelihood of an emotion. See more information.

Facial Expressions

Measure of focus based on the head orientation
Brow Furrow
Both eyebrows moved lower and closer together
Brow Raise
Both eyebrows moved upwards
Cheek Raise
Lifting of the cheeks, often accompanied by "crow's feet" wrinkles at the eye corners
Chin Raise
The chin boss and the lower lip pushed upwards
The lip corners tightened and pulled inwards
Eye Closure
Both eyelids closed
Eye Widen
The upper lid raised sufficient to expose the entire iris
Inner Brow Raise
The inner corners of eyebrows are raised
Jaw Drop
The jaw pulled downwards
Lid Tighten
The eye aperture narrowed and the eyelids tightened
Lip Corner Depressor
Lip corners dropping downwards (frown)
Lip Press
Pressing the lips together without pushing up the chin boss
Lip Pucker
The lips pushed foward
Lip Stretch
The lips pulled back laterally
Lip Suck
Pull of the lips and the adjacent skin into the mouth
Mouth Open
Lower lip dropped downwards
Nose Wrinkle
Wrinkles appear along the sides and across the root of the nose due to skin pulled upwards
Lip corners pulling outwards and upwards towards the ears, combined with other indicators from around the face
Left or right lip corner pulled upwards and outwards
Upper Lip Raise
The upper lip moved upwards

Emoji Expressions

Mouth opened and both eyes closed
Smiling, mouth opened and both eyes opened
Smiling and both eyes opened
Either of the eyes closed
The lips puckered and both eyes opened
Stuck Out Tongue
The tongue clearly visible
Stuck Out Tongue and Winking Eye
The tongue clearly visible and either of the eyes closed
The eyebrows raised and the mouth opened
The eyebrows raised and both eyes widened
Left or right lip corner pulled upwards and outwards
Frowning, with both lip corners pulled downwards
The brows furrowed, and the lips tightened and pressed
Neutral face without any facial expressions

Using the Metrics

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.

Face Tracking and Head Angle Estimation

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).

Interocular Distance

The distance between the two outer eye corners.