Active Liveness Behavior
On the active liveness SDK, users should follow instructions to be verified as a live person. Some poses will consider the user to not use attributes during taking the selfie to prevent false checking. It has 3 random head pose combinations containing pitch, yaw, roll movement; blinking eyes movement, and smile.

Pose Guideline

The active liveness pose will capture the selfie movement based on the instruction. If the user do wrong pose instruction, it will be rejected and get vailed verification. User should pass the verification before 10 second, if the verification time is up, the process will be repeated from the beginning.
To check the head pose movement, we use pitch as X-axis movement, yaw as Y-axis movement, roll as Z-axis movement.
Head Movement Axis
The pitch movement consists of nod up and nod down, the yaw movement consists of a look right and a look left pose, and for the roll movement consist of nod right and nod left. To change the pose instruction, please check our string.xml details. You are able to modify the wording for a better experience. Here are the list of possible head random poses on Nodeflux Active Liveness.
Position
Explanation
Text
Look right (right orientation)
Move head to look right (Y-axis) minimum 30 degree
Look left (right orientation)
Move head to look left (Y-axis) minimum 30 degree
Nod right (shake right)
Nod the head to right (Z-axis to the right movement)
Nod left (shake left)
Nod the head to right (Z-axis to the left movement)
Nod up
move head to look up (X-axis up movement)
Nod down
move head to look down (X-axis left movement)
Blink eyes
Blink both eyes
Blink left eye
Blink the left eye
Blink right eye
Blink the right eye
Smile
Smile with open mouth and the face still on frontal pose

Face Occlusion

We use a landmark for validating the instruction. The object occlusion can contribute false positives in the mouth pose. Such as face masks, sunglasses, and normal glasses.
  • Face masks most likely though not absolutely will affect mouth pose detection by giving the low probability of smile detection. Different masks can produce different pose quality readings and can cause false positives. On our benchmark, the mask types that we test are medical masks and n95 type with solid color with no color/printing texture.
Low Possibility False Positive
High Possibility False Positive
  • Sunglasses will affect eye pose detection. Using dark sunglasses interfere with eye detection, however, transparent sunglasses can cause false detection. It means there will be a possibility of using sunglasses the verification is passed.
  • Eyeglasses, although it is transparent, the possibility of occlusion will be found on glare light effect on the glasses. Avoid this condition so that the possibility of false detection can be decreased.
Last modified 24d ago