Research Aspects - Driver Drowsiness Detection: Systems and Solutions - page 29

Fig. 4.3 Examples of eigenfaces

(Fig. 4.4 ). Every object usually contains a specific set of local minima and maxima
that can be used as characteristic to describe that object, a feature that has been used
in various research studies in the literature [ 6 , 25 ].

Scale-Invariant Feature Transform (SIFT) SIFT is an algorithm in computer
vision to detect and describe local features in images. The SIFT feature descriptor is
invariant to uniform scaling, orientation, and partially invariant to affine distortion
and illumination changes. Once it has been computed, an image is transformed into
a large collection of feature vectors, each of which is invariant to image translation,
scaling, and rotation, partially invariant to illumination changes and robust to local
geometric distortion. These features share similar properties with neurons in inferior
temporal cortex that are used for object recognition in primate vision. Key locations
are defined as maxima and minima of the result of difference of Gaussian (DoG)
function applied in scale space to a series of smoothed and re-sampled images. Low
contrast candidate points and edge response points along an edge are discarded.
Dominant orientations are assigned to localized key points. These steps ensure that
the key points are more stable for matching and recognition. SIFT descriptors, robust