Face Detection and Modeling for Recognition - Biometrics Research ...

esolutions and of poor quality (i.e., to extract features under uncertainty). However,
the appearance-based recognition techniques have their own limitations in recognizing
human faces in images with wide variations in 3D head pose and in illumination [38].
Hence, in order to overcome variations in pose, a large number of face recognition
techniques have been developed to take into account the 3D face shape, extracted
either from a video sequence or range data.
As for overcoming the variations in
illumination, several studies have explored features such as edge maps (e.g., eigenhills
and eigenedges in [42]), intensity derivatives, Gabor-filter responses [43], and
the orientation fields of intensity gradient [44]. However, none of these approaches
by themselves lead to satisfactory recognition results. Hence, the explicit 3D face
model combined with its reflectance model is believed to be the best representation
of human faces for the appearance-based approach [43].
1.2 Semantic Facial Components
Face recognition technology provides useful tools for content-based image and video
retrieval based on a semantic (high-level) concept, i.e., human faces. Is all face processing
holistic [45] Some approaches, including feature-based and appearance-based
[46] methods, emphasize that internal facial features (i.e., pure face regions) play the
most important role in face recognition. On the other hand, some appearance-based
methods suggest that in some situations face recognition is better interpreted as
head recognition [8], [31]. An example supporting the above argument was demonstrated
for Clinton and Gore heads [8] (See Fig. 1.5(a)).
While the two faces in
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