Exhibit 15.19 Feature detection.
(Source: Realeyes).
Facial Action Coding System (FACS) is a method of measuring facial
expressions and describing observable facial movements. It breaks down facial expressions into elementary
components of muscle movement called Action Units (AUs).
Labelled as AU0, AU1, AU2 etc., AUs correspond to individual muscles or muscle groups. They
combine in different ways to form facial expressions. The analysis of the AUs of a facial image, therefore,
leads to the detection of the expression on the face.
Automated facial coding (AFC) powered by machine learning algorithms and webcams,
has become popular across numerous sectors, including marketing analytics. It typically involves
a 3-step process:
- Face detection: When you take a photo with a camera or a smartphone, you may notice boxes
framing the faces of the individuals in the photo. These boxes are appearing because your camera
is using face detection algorithms. AFC uses the same technology to detect faces.
- Facial landmark detection (Exhibit 15.19): AFC then detects facial landmarks, such as eyes and eye
corners, brows, mouth corners, and nose tip, and creates a simplified face model that matches the actual face,
but only includes the features required for coding.
- Coding: Machine learning algorithms analyse the facial landmarks and translate them into action
unit codes. The combinations of AUs are then statistically interpreted to yield metrics for facial expressions.
