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We have considered the problem of tracking and recognition using a three dimensional representation of human faces. First we present a review of the research in the tracking and recognition fields including a list of several commercially available face tracking and recognition systems. Next, two algorithms are described: one for tracking faces from observed images and one for recognition of faces from observed geometries. The tracking algorithm uses 3D shape and texture of a human face to estimate the changing position and orientation of a real face in a video image sequence. The recognition algorithm uses principal component analysis (PCA) of range images generated from the 3D shape of a human face to create a database of low-dimensional face representations for efficient recognition. Range images are robust to illumination and texture variations and thus avoid some of the current limitations in face recognition.
Face Recognition, Face Tracking
Date of Defense
April 10, 2003.
A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Gordon Erlebacher, Professor Directing Thesis; Anuj Srivastava, Professor Co-Directing Thesis; Kyle Gallivan, Committee Member.
Florida State University
Hesher, M. C. (2003). Automated Face Tracking and Recognition. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-4074