Graduation Year

2006

Document Type

Thesis

Degree

M.S.Cp.E.

Degree Granting Department

Computer Science and Engineering

Major Professor

Dmitry B. Goldgof, Ph.D.

Co-Major Professor

Rangachar Kasturi, Ph.D.

Committee Member

Sudeep Sarkar, Ph.D.

Keywords

face recognition, face dynamics, non-rigid motion, optical flow, elasticity

Abstract

Research on video-based face recognition has started getting increased attention in the past few years. Algorithms developed for video have an advantage from the availability of plentitude of frames in videos to extract information from. Despite this fact, most research in this direction has limited the scope of the problem to the application of still image-based approaches to some selected frames on which 2D algorithms are expected to perform well. It can be realized that such an approach only uses the spatial information contained in video and does not incorporate the temporal structure.Only recently has the intelligence community begun to approach the problem in this direction. Video-based face recognition algorithms in the last couple of years attempt to simultaneously use the spatial and temporal information for the recognition of moving faces. A new face recognition method that falls into the category of algorithms that adopt spatio-temporal representation and utilizes dynamic information extracted from video is presented. The method was designed based on the hypothesis that the strain pattern exhibited during facial expression provides a unique "fingerprint" for recognition. First, a dense motion field is obtained with an optical flow algorithm. A strain pattern is then derived from the motion field. In experiments with 30 subjects, results indicate that strain pattern is an useful biometric, especially when dealing with extreme conditions such as shadow light and face camouflage, for which conventional face recognition methods are expected to fail. The ability to characterize the face using the elastic properties of facial skin opens up newer avenues to the face recognition community in the context of modeling a face using features beyond visible cues.

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