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Real-time location and parameterization of eyes in an image sequence and the detection of their point-of-gaze

University of British Columbia

The determination of point-of-gaze of an imaged subject is the main focus of this thesis. Digitized image sequences from a single camera are analysed to determine the point-of-gaze of a subject without manual setup of algorithms for that subject or calibration of the system. A novel feature of this work is that eyes are initially located by using motion to characterize possible Eye-events. A priori knowledge of an eye's motions, spatial orientation and characteristics are used to find Eye-events. Deformable templates are used to parameterize eyes in an image sequence into a parameterization vector using a 2-D model of the eye and face. The pupil, iris, skin, hair, upper and lower eyelids of a subject are parameterized to form a knowledge base for that subject. The tracking of these eye parameters is performed similarly to their initial parameterization. Point-of-gaze is determined by applying the 2-D eye and face parameters to a 3-D model of the eye. Eye Location, Parameterization, and Tracking algorithms are run using both Matlab on a SPARC 5 workstation and C on a TMS32C40 parallel processing network. The algorithms described in this thesis run equally well on both of these systems. The algorithms are shown to be able to locate eyes in a series of images over a wide range of eye scales. The accuracy of the algorithms is analysed on the SPARC system by having a subject view sets of targets at known locations. The system's determination of a person's point-of-gaze was compared to the actual target positions, showing an error of 4.01° or less along the horizontal axis and 4.99° or less along the vertical axis for targets at or above the eye level of the subject. The accuracy of the system lowers to 5.41° or less along the horizontal axis and 15.76° or less along the vertical axis for targets below the eye level of the test subject. The C40 parallel processing system which runs these algorithms, automatically switching between the Location, Parameterization, and Tracking modes of operation, is described in detail. This system was able to track parameterized eye features at up to 15 frames per second (fps) during saccadic eye motion without a requirement for a fixed head position. The timing of these algorithms on the C40 network is analysed.

Thesis/Dissertation

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2009-05-19

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http://hdl.handle.net/2429/7944

Electrical and Computer Engineering

University of British Columbia

UBCV

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