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

such vision-based systems are (i) detection of human faces, (ii) construction of face
models/representations for recognition, and (iii) identification of human faces.
Detection of human faces is the first step in our proposed system. It is also the
initial step in other applications such as video surveillance, design of human computer
interface, face recognition, and face database management. We have proposed a face
detection algorithm for color images in the presence of various lighting conditions
as well as complex backgrounds. Our detection method first corrects the color bias
by a lighting compensation technique that automatically estimates the statistics of
reference white for color correction. We overcame the difficulty of detecting the lowluma
and high-luma skin tones by applying a nonlinear transformation to the Y CbCr
color space. Our method detects skin regions over the entire image, and then generates
face candidates based on the spatial arrangement of these skin patches. Next, the
algorithm constructs eye, mouth, and face boundary maps for verifying each face
candidate. Experimental results have demonstrated successful detection of multiple
faces of different size, color, position, scale, orientation, 3D pose, and expression in
several photo collections.
Construction of face models is closely coupled with recognition of human faces,
because the choice of internal representations of human faces greatly affects the design
of the face matching or classification algorithm. 3D face models can help augmenting
the training face databases used by the appearance-based face recognition approaches
to allow for recognition under illumination and head pose variations. For recognition,
We have designed two methods for modeling human faces based on a generic 3D face
model. One requires individual facial measurements of shape and texture (i.e., color
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