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

algorithm can also provide geometrical facial features for face recognition. Merging
the geometrical features and holistic texture (appearance-based) features is believed
to be a promising method of representing faces for recognition [59], [60]. Therefore,
we believe that a seamless combination of face detection and recognition algorithms
has the potential of providing a high performance face identification algorithm.
Hence, we have proposed a face detection algorithm for color images, which is
able to generate geometrical as well as texture features for recognition. Our approach
is based on modeling skin color and extracting geometrical facial features. The skin
color is detected by using a lighting compensation technique and a nonlinear color
transformation. The geometrical facial features are extracted from eye, mouth, and
face boundary maps. The detected faces, including the extracted facial features, are
organized as a graph for modeling and recognition processes. Our algorithm can detect
faces under different head poses, illuminations, and expressions (see Fig. 1.14(a)), and
family photos (see Fig. 1.14(b)). However, our detection algorithm is not designed
for detecting faces in gray-scale images, cropped face images (see Fig. 1.15(a)) and
faces wearing make-up or mask (see Figs. 1.15(b) and (c)).
1.5 Face Modeling for Recognition
Our face recognition systems are based on 3D face models. 3D models of human faces
have been widely used to facilitate applications such as video compression/coding,
human face tracking, facial animation, augmented reality, recognition of facial expression,
and face recognition. Figure 1.16 shows two graphical user interfaces of a
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