D.B. Megherbi, A.J. Boulenouar, and V. Rajagopalan (USA)
Canny edge detection, Multi-connected regions, Euclidean distance, Message Passing, Contour matching, Face Shape Representation and Recognition, Parallel Computing, Network of Workstations (NOW), Distributed Systems.
This work deals with a new distributed face recognition technique based on transformation invariant conformal mapping. Face recognition using digital images is constrained by several factors like rotation (in and out of plane), scaling, and is usually operated under strict lighting conditions. In a previous work [24][25], through a conformal mapping process, we demonstrated the ability to 1) recognize shapes, and 2) concisely represent shape boundaries using a set of polynomial coefficients derived in the mapping process. In this work we illustrate how these previous results can be applied to face recognition. Additionally, in the approach outlined herein, a syntactic representation is formed for polygonal and non polygonal shapes representing a given face features whose representation we desire to extract and reproduce compactly. In particular, in this paper we focus on the face outline, left eyebrow, right eyebrow, left eye, right eye, nose, curve from the lower nose to upper lips, and lips as face features. Some of these face feature are grouped together and then processed in parallel in a distributed network of workstations via a Message Passing Interface running over TCP/IP. In particular, we show that a master-salve paradigm is used here to implement the proposed parallel and distributed algorithm and is based on a distributed-memory approach. Tests were performed with 1, 2 and three workstations. The algorithm assumes no constraints in the lighting, and the size of the window that has the face. This work deals with face representation and recognition and face segmentation is not the subject of this paper. We finally show the potential of the proposed generalized technique in its ability to handle both polygonal, non-polygonal and mixed polygonal/non-polygonal object shapes. We also show that the proposed algorithm achieves high recognition rates for rotations “in” the plane, translation, and scaling and is robust in noisy environments.
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