Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: This paper presents a new color face image database
for benchmarking of automatic face detection algorithms and human
skin segmentation techniques. It is named the VT-AAST image
database, and is divided into four parts. Part one is a set of 286 color
photographs that include a total of 1027 faces in the original format
given by our digital cameras, offering a wide range of difference in
orientation, pose, environment, illumination, facial expression and
race. Part two contains the same set in a different file format. The
third part is a set of corresponding image files that contain human
colored skin regions resulting from a manual segmentation
procedure. The fourth part of the database has the same regions
converted into grayscale. The database is available on-line for
noncommercial use. In this paper, descriptions of the database
development, organization, format as well as information needed for
benchmarking of algorithms are depicted in detail.