Biometrics are measurable characteristics specific to an individual. They
can be used to identify individuals. The use of biometrics for identification has
the potential to make our lives easier, and the world we live in a safer place.
While numerous different types of exploitable biometrics exist, facial
identification is highly pursued because the data can be captured at a distance
without requiring the subject s active cooperation. While traditionally 2D images
of faces have been used, 3D scans that contain both 3D data and registered color
are becoming easier to acquire.
Before 3D face images can be used to identify an individual, they require
some form of initial alignment information, typically based on facial feature
locations. This thesis proposes and analyzes a multimodal approach to automatic
facial feature detection. After beginning with a discussion on biometrics and the
role of automatic facial feature detection, we provide a comparative evaluation of
3D sensors. We follow this by a discussion of the algorithm s performance when
constrained to frontal images and an analysis of its performance on a more
complex dataset with significant head pose variation.