Release notes

• A novel experimental tracking algorithm – VNN – introduced

The new algorithm minimizes jitter, increases tracking accuracy and robustness but reduces tracking performance (speed). It is demonstrated in ShowcaseDemo and FaceTracker2 samples via Ultra tracking configuration.

• New neural network runner provided – Intel’s OpenVINO™

Significantly improves the performance of age estimation, face recognition and face tracking with VNN algorithm on Intel 64-bit processors.

• Ear tracking

Additional 24 feature points on ears are now tracked (12 points per ear). Ear tracking is configurable through the tracker configuration file or API.

• Iris tracking

Face data from tracker and detector now includes information about iris diameter.

• VisageConfiguration API introduced

It is now possible to modify specific tracker configuration parameters via an interface during tracking.

• Age estimation accuracy improved

• Ear tracking introduced: Tracker can track 24 additional feature points on ears (12 points per ear); Configurable via configuration file.
• Iris tracking: Face data from tracker and detector now includes information about iris diameter.
• Age estimation accuracy improved
• Tracking performance (speed) improved

Platforms: Windows, Mac OS X, Linux, RedHat

• Improved smoothing filter
  Smoothing of feature points is performed using multiple filters. For still face, higher amount of smoothing is applied while fast movements are less smoothed in order to avoid noticeable delay. Increased stability of feature points and head position especially in profile and half-profile pose.
• Refactoring of frame preprocessing resulting in more stable FPS and improved accuracy on high-resolution frames
  The core tracking loop was reimplemented to make the tracking frame rate less dependent on the size of the face in the image. This fixes performance drops in cases where the face takes up a small portion of the frame. Additionally, noise introduced by resizing of higher-resolution frames is reduced which results in more stable tracking.

Platforms: HTML

• Improved smoothing filter
  Smoothing of feature points is performed using multiple filters. For still face, higher amount of smoothing is applied while fast movements are less smoothed in order to avoid noticeable delay. Increased stability of feature points and head position especially in profile and half-profile pose.
• Refactoring of frame preprocessing resulting in more stable FPS and improved accuracy on high-resolution frames
  The core tracking loop was reimplemented to make the tracking frame rate less dependent on the size of the face in the image. This fixes performance drops in cases where the face takes up a small portion of the frame. Additionally, noise introduced by resizing of higher-resolution frames is reduced which results in more stable tracking.
• API upgraded to use memory arrays
  API for fetching tracking data has been modified to return memory arrays. Improves performance and simplifies memory managment of tracked data.

Platforms: iOS, Android

• Improved smoothing filter
  Smoothing of feature points is performed using multiple filters. For still face, higher amount of smoothing is applied while fast movements are less smoothed in order to avoid noticeable delay. Increased stability of feature points and head position especially in profile and half-profile pose.
• Refactoring of frame preprocessing resulting in more stable FPS and improved accuracy on high-resolution frames
  The core tracking loop was reimplemented to make the tracking frame rate less dependent on the size of the face in the image. This fixes performance drops in cases where the face takes up a small portion of the frame. Additionally, noise introduced by resizing of higher-resolution frames is reduced which results in more stable tracking.
• ShowcaseDemo introduces example of tracking from video including source code.