A
face detection algorithm very robust against illumination, focus and
scale variations in input images has been developed based on the
edge-based image representation. The multiple-clue face detection
algorithm developed in our previous work has been employed in
conjunction with a new decision criterion called idensity rule, where
only high density clusters of detected face candidates are retained as
faces. As a result, the occurrence of false negatives has been greatly
reduced. The robustness of the algorithm against circumstance variations
has been demonstrated.
The automatic recognition of human faces
presents a significant challenge to the pattern recognition research community,
human faces are very similar in structure with minor differences from person to
person. They are actually within one class of “human face”. Furthermore,
lighting condition changes, facial expressions, and pose variations further
complicate the face recognition task as one of the difficult problems in pattern
analysis. This paper proposed a novel concept, “faces can be recognized using
line edge detection”. A face pre filtering technique is proposed to speed up the
searching process. It is a very encouraging finding that the proposed face
recognition technique has performed superior to the most of the existing
comparison experiments.
An edge
in an image is a contour across which the brightness of the image
changes abruptly. In image processing, an edge is often interpreted as
one class of singularities. In a function, singularities can be
characterized easily as discontinuities where the gradient approaches
infinity. However, image data is discrete, so edges in an image often
are defined as the local maxima of the gradient. Edge detection is an
important task in image processing. It is a main tool in pattern
recognition, image segmentation, and scene analysis. An edge detector is
basically a high pass filter that can be applied to extract the edge
points in an image.
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