TRACKING THE FACE:-
Our basic
idea is that individual search windows help each other to track their features.
From the known geometric relationship between the features in a face, a lost
search window can be repositioned with help from features that are still
tracking. We use a two-dimensional model of the face in which features for
tracking are joined to form a small network. The reference vectors connecting
the features are derived from a single image automatically by the system or by
a human operator. Figure 1 shows a face with boxes marking the nine (9)
tracking features. We use the iris, the corners of the eyes, the eyebrows and
the middle and corners of the mouth.The sizes of the boxes shown are the
actual template sizes (16x16 pixels).The line connections features assist the other features for readjusting the search
windows. We also use several templates to track features that can change their
appearance. For example the eyes can be open or closed. In such cases we use
three (3) templates for the different states (opened, closed and
half-open-closed) of the eyes simultaneously. This makes it possible to
determine the state of the tracking features e.g. an eye is open or the mouth
is closed.
As discussed
earlier if a person turns their head the distortions of all the templates
increases greatly. In this situation some features may disappear and others may
change their shade and appearance. It is difficult to determine whether search
windows are tracking correctly or incorrectly. Lost search windows influence
the tracking position of the other search windows.A situation can easily arise
in which the system will loose the entire face. Simple thresholding of the
distortion is insufficient to distinguish the lost windows from the tracking
ones.An approach that can cope with noisy data is needed. Kalman filters were
used solve this problem.
The Kalman filter is a
recursive linear estimator,which merges the measurement of sensors observing
the environment with a prediction that is derived from a system model.The
Kalman filter is used in many applications such as navigation of planes,
missiles and mobile robots where uncertain measurements from sensors that
observe landmarks are used to localize a vehicle. By merging sensor
information, the Kalman filter guarantees an optimal estimate of the sensor
data in terms of a minimum mean-square error if an appropriate system model is
used.All sensor data has co variances associated with it,which indicate the
reliability of the data.The output of the filter also has a covariance,so the
control system does not only obtain an estimate, but it also knows the
reliability of the estimate.
Using Kalman
filtering yields a system, which copes with head rotations of about 30 degrees
during facing tracking.Further robustness was added to the face tracking by
implementing dynamic search regions,which look for a feature inside a specific
area of the image.The size of the search region is dependent on the variance
of the features (determined from the Kalman filter).We also extended our 2D
model of the face to allow for tilting.This extra technique allow the head to
be rotated up to 60 degrees, tilted acutely from side to side,and enables
quick recovery even when all the tracking features have been lost.
Another
improvement considered is to grab templates of the features dynamically while
the system is tracking the face.This would not only improve the tracking,but
the system would also cope with much greater ranges of changing illumination.
It is planned to create a dynamic face model that adapts to the gathered data.Such a dynamic system would learn how to track the face of an unknown person.The system would be initially provided with several generic faces including
startup templates and face geometries.It selects the most similar model for
the unknown person and then learns the exact templates and geometry.
A gesture
recognition module is implemented which runs in parallel with the face-tracking
module at video frame rate (30Hz). This approach adopted produces reliable
results and is robust to noise. The system accurately discriminates between 13
different gestures. Even though some gestures are quite similar to each other.
CONCLUSION:
The humanoid research is an
approach to understand and realize flexible complex interactions between
robots, environment and humans.
A humanoid robot is an ideal
tool for the robotics research; First of all it introduces complex interactions
due to its complex structure. It can be involved in various physical dynamics
by just changing its posture without need for a different experimental
platform. This promotes a unified approach to handling different dynamics.
Since it resembles humans, we can start by applying our intuitive strategy and
investigate why it works or not. Moreover, it motivates social interactions
such as gestural communication or cooperative tasks in the same context as the
physical dynamics. This is essential for three-term interaction, which aims at
fusing physical and social interaction at fundamental levels.
Integrating
human body components such as human prostheses for upper limbs, and
anthropomorphic control and behavioral schemes can approach the humanoid
robotics.
The Gesture
Recognizer module that runs in parallel with the face-tracking module is
capable of recognizing a wide variety of gestures based on head movements.
Gesture recognition is robust due to the statistical approach we have adopted.
In future the plan is to record and analyze the head gestures of a large sample
of people. The plan is also to explore the prospect of allowing the machines to
learn gestures based on observation.
The ultimate
aim is to use the facial gesture recognition system in a robotic system for the
disabled. The interface will allow disabled persons to feed themselves by using
facial gestures to communicate with the helping robot.
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