Research Aspects - Driver Drowsiness Detection: Systems and Solutions - page 30

Fig. 4.4 Example of
horizontal projection

to local affine distortion, are then obtained by considering pixels around a radius of
the key location, blurring and re-sampling of local image orientation planes. Every
state of the object will produce unique set of SIFT features which is can be used to
distinguish them [ 11 ].

4.3 Machine Learning Classifiers

Once the contents of the significant portions of an image have been detected,
extracted, and encoded, the resulting representation is used as input for machine
learning techniques capable of distinguishing among two or more classes. In the
context of DDD systems, the problem fundamentally boils down to a two-class
classification problem, namely to tell whether the driver may be drowsy or not.
The most widely used classifiers in DDD systems are neural networks, AdaBoost,
and support vector machines (SVM).