 |
Filename: range-of-tracked-motion.mpeg (1.6Mb) & speed-n-robustness.mpeg (0.9Mb)
Description: Nouse at work.
See these demos first to see range of motion and speed & robustness of Nouse. |
 |
Filename: closeview-robustness.mpeg (4Mb), overview-speed.mpg (3Mb) & more
Description: доставка цветов Новокузнецк - https://florafox.com/ru/novokuznyetsk-68.
These demos show the range of motion and the speed & robustness of the 3D Nouse-based Stereo Face Tracking
|
 |
Filename: Gclef.avi (260Kb) & Gclef-ani.gif
Description: NousePaint at work.
Test - The user rotates his head only! (the shoulders do not move) |
 |
Filename: home-of-nouse-with-distractions.avi (1.2Mb)
Description: NousePaint at work.
Test - The user writes a sentence with the nose, with his colleagues bothering him. |
 |
Filename: ImmersiveEnvWNouse.mpg (4Mb)
Description: Navigating in Virtual 3D Worlds with Nouse (in a mouse mode).
In this demo, the user rotates her head to follow a moving object (a car) in a virtual 3D world. Nouse tracks the user's nose and adjusts the first-person 3D view according to the rotation of the head: lifting the head up causes the view to go higher and so on. |
 |
Filename: PlayingWNouseBF.mpg (3Mb)
Description: Precise aiming with the nose (Nouse in a joystick mode)
A user plays a well-known BubbleFrenzy game, the goal of which is to aim the turret to match the bubbles. Traditionally played with a mouse or key presses, it can now be played in more natural way by pointing the direction with the nose.
Players say: "Playing the game with Nouse is not only more fun, but is also less tiring!" - Some users experienced severe wrist fatigue when they played the game using a mouse for longer than 15 minutes. This does not happen when they play it with with Nouse.
Using the nose to aim the turret is found very natural, while the precision of aiming with the nose was as good as with mouse. |
|
Image collection
You can write or operate with Nouse as with a joystick or a chalk.
|
 
|
|
You can navigate in Windows environment hands-free.
More drawings made hands-free by nose are here.
|
|
|
|
2. Other Perceptual Vision tools
2.1 Blink detection using second-order change detection
 |
Filename: bt-result.avi (440Kb) & NousePP.avi (160Kb) & more
Description: Blink Detection uses second-order change detection to detect eye blinking, switches Nouse On/Off by double-blinking.
See also image below.
|
| To see how second-order change detection helps detecting blinks in moving heads, watch:
Filename: ddI-blinking-face.avi
Description: ddI-blinking-face.avi
Filename: floppy-ani.gif
Description: in this example, of a floppy disk moves from left to right with a protective cover sliding from an open to close position.
|
In the bottom row:
First image (on the left) - this is what you get using ordinary (first order) change detection.
Second and third images - this is what you can remove from the first image.
Right most images - this is what you can get to detect local (second-order) change. |
2.2 Skin detection for face tracking and augmented reality
 
Filename: conducting-ani.gif, 4-4.avi, faces.avi & more
Description:
We have examined many non-linear colour spaces for optimal skin model representation. The best result were obtained, using Perceptual Uniform Colour Space, which is the space that approximates the colours the way humans perceive them.
Motion information allows to filter out the spurious skin-looking regions
2.3 Face Detection and Tracking with Multiple Cameras using three-channel video representation
Can you find six different webcams on the picture at right? (They all have different colour adjustment properties)
|
All of them are running at the same time,
detecting and tracking a face:
To see the result (with lights off and on) view these images:
6cams-fd-lights-off.gif
6cams-fd-lights-on.gif
6cams-fd-lights-on1.gif
Note that switching on the lights is not detected as a motion change - which is due to the non-linear change detection.
Also note that, even when one channel (or one camera) fails detecting a face, the others in most cases do not.
This allows one to detect and track a face very robustly (regardless of face orientation, etc)
|
 |
|
|
| |
|
|
| 2.4 On-line face memorization and recognition for user registration and identification
You can also see the animated gif (600Kb) and AVI movie (2Mb - watch in 50% size to see the entire picture) which show the snapshots of the program during the on-fly memorization and retrieval of faces from video and face database.
The sequence of actions in the movie is the following:
- a face of the current user is memorized,
- 62 faces are loaded from the face database (using face_data-BioID.txt face list - first image is memorized used)
- recognition is performed on the same 62 persons shown with second image of each person from the face_data-BioID.txt list used
- video is back on and the user is again (and still) recognized every time he blinks.
The log file with recognition statistics for this run is given here.
The content of the memory (represented by the memory synaptic matrix) shown in the right top image. Grey image means nothing is stored - all weights are zero. As more and more face are stored the matrix approaches the identity matrix. By analyzing this memory image, one can always analytically estimate the quality of face retrieval - more about the recognition at this website.
|
