17-05-2014, 10:32 AM
Real-Time Multi-Face Recognition and Tracking Techniques Used for the Interaction between Humans and Robots1
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Introduction
The technology of biometric recognition systems for personal identification commonly
manipulate the input data acquired from irises, voiceprints, fingerprints, signatures, human
faces, and so on. The recognition of irises, voiceprints, fingerprints, and signatures belongs
to the passive methods that require the camera with a high resolution or capture people‘s
biometric information at a short range. These methods are not suitable for our person
following robot to be developed, because they cannot provide convenience for users. Face
recognition belongs to one of the active methods that users need keep away from a camera
at a certain distance only. In this chapter, face recognition is regarded as a kind of human
computer interfaces (HCIs) that are applied to the interaction between humans and robots.
Thus, we attempt to develop an automatic real-time multiple faces recognition and tracking
system equipped on the robot that can detect human faces and confirm a target in an image
sequence captured from a PTZ camera, and keep tracking the target that has been identified
as a master or stranger, then employ a laser range finder to measure a proper distance
between target’s owner and the robot.
Hardware description
The following describes the hardware system of our experimental robot whose frame size is
40 cm long, 40 cm wide, and 130 cm high as Fig. 1 shows. The robot has three wheels; the
motors of two front wheels driving the robot to move forward/backward and turn
left/right, whereas one rear wheel without dynamic power is used for supporting the robot
only. By controlling the turning directions of the two front wheels, we can change the
moving direction of the robot. In order to help the robot to balance, we settle one ball caster
in the rear.
Face detection
The face detection is a crucial step of the human face recognition and tracking system. For
enabling the system to extract facial features more efficiently and perfectly, we must narrow
the range of face detection first, and the performance of the face detection method can’t be
too low. In order to detect human faces quickly and accurately, we take advantage of an
AdaBoost (Adaptive Boosting) algorithm to build a strong classifier with simple rectangle
features involved in an integral image (Viola & Jones, 2001). Therefore, no matter what the
size of a rectangle feature we use, the execution time is always constant. Some rectangle
features are fed to the strong classifier that can distinguish positive and negative images.
Face recognition
Through the face detection procedure, we can take face images that are then fed to the face
recognition procedure. To begin with, we execute the image normalization to make the sizes
of face images be the same. The intensity of the images will be also adjusted for reducing the
lighting effect. After the size normalization and intensity adjustment, we subsequently
perform the feature extraction process to obtain the feature vector of a face image. The idea
of common vectors was originally introduced for isolated word recognition problems in the
case that the number of samples in each class is less than or equal to the dimensionality of
the sample space. The approaches to solving these problems extract the common properties
of classes in the training set by eliminating the differences of the samples in each class. A
common vector for each individual class is obtained from removing all the features that are
in the directions of the eigenvectors corresponding to the nonzero eigenvalues of the scatter
matrix of its own class. The common vectors are then used for pattern recognition. In our
case, instead of employing a given class’s own scatter matrix, we exploit the within-class
scatter matrix of all classes to get the common vectors. We also present an alternative
algorithm based on the subspace method and the Gram-Schmidt orthogonalization
procedure to acquire the common vectors. Therefore, a new set of vectors called the
discriminative common vectors (DCVs) will be used for classification, which results from
the common vectors. What follows elaborates the algorithms for obtaining the common
vectors and the discriminative common vectors (Gulmezoglu et al., 2001)
Conclusion and feature works
In this chapter, we present a completely automatic real-time multiple faces recognition and
tracking system installed on a robot that can capture an image sequence from a PTZ camera,
then use the face detection technique to locate face positions, and identify the detected faces
as the master or strangers, subsequently track a target and guide the robot near to the target
continuously. Such a system not only allows robots to interact with human being
adequately, but also can make robots react more like mankind.
Some future works are worth investigating to attain better performance. In the face recognition
procedure, if the background is too cluttered to capture a clear foreground, the recognition rate
will decrease. Because most of our previous training samples were captured in a simple
environment, sometimes static objects in the uncomplicated background are identified as the
foreground. We can increase some special training samples in a cluttered background to lower
the miss rate during the face detection. Of course, it will raise the face recognition accuracy,
but need a lot of experiments to collect special and proper training samples.