Fusion of Visual and Thermal Face Recognition Techniques: A ...

Since eyes in visual images have strong edges, and contain eyeballs inside and clear
corners, it is relatively easy to process detection of the eyes. Although researcher and
scientists have developed eye detection algorithms for visual images, it is still
questionable if the eyes (the center of the eyeballs) can be detected when individuals
close their eyes, are affected by reflection of lights, or are wearing sunglasses. The eyes
(especially the eyeballs) in the visual images are relatively robust features more than any
other features such as the mouth, nose, or the size of the head. Jeffrey Huang et al [48]
proposed an eye detection method using natural selection (Genetic Algorithm), learning
(Decision Trees), and their interactions. Lam et al [49] used a corner detection scheme
which can provide information on corner orientations and angles in addition to corner
location. Mariani [50] proposed a subpixellic eye detection scheme to reduce greatly the
number of possible scales used during the face recognition process. Huang et al [51]
proposed a scheme for face location and accurate eyes detection, which is based on
multiple evidence, including facial component structure, texture similarity, component
feature measurement, the Hough transform and contour matching. After detecting faces,
they proposed a precise eye location combining contour and region information extracted
from a zoomed image. Jeffrey Huang. et al [52] proposed an approach for the eye
detection task using optimal wavelet packets for eye representation and the Radial Basis
Function (RBF) for subsequent classification of facial areas as eye vs. non-eye regions.
Entropy minimization is the driving force behind the deviation of optimal wavelet
packets.
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