1 research outputs found
Face Recognition from Face Signatures
This thesis presents techniques for detecting and recognizing faces under various
imaging conditions. In particular, it presents a system that combines several
methods for face detection and recognition. Initially, the faces in the images are
located using the Viola-Jones method and each detected face is represented by
a subimage. Then, an eye and mouth detection method is used to identify the
coordinates of the eyes and mouth, which are then used to update the subimages
so that the subimages contain only the face area. After that, a method based
on Bayesian estimation and a fuzzy membership function is used to identify the
actual faces on both subimages (obtained from the first and second steps). Then, a
face similarity measure is used to locate the oval shape of a face in both subimages.
The similarity measures between the two faces are compared and the one with
the highest value is selected.
In the recognition task, the Trace transform method is used to extract the
face signatures from the oval shape face. These signatures are evaluated using
the BANCA and FERET databases in authentication tasks. Here, the signatures
with discriminating ability are selected and were used to construct a classifier.
However, the classifier was shown to be a weak classifier. This problem is
tackled by constructing a boosted assembly of classifiers developed by a Gentle
Adaboost algorithm. The proposed methodologies are evaluated using a family
album database