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Hand geometry based user identification using minimal edge connected hand image graph

Hand geometry based user identification using minimal edge connected hand image graph

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In a previously reported work, the user's hand is represented as a weighted undirected complete connected graph and spectral properties of the graph are extracted and used as feature vectors. To reduce the complexity in representing the hand image as a complete connected graph and to achieve the higher identification rate, the hand image is sought to be represented as minimal edge connected graph. The experiments are conducted separately for 16 topologies of minimal edge connected graph selected empirically to investigate the performance of the hand-geometry system. The prominent edges of hand image graph are identified experimentally by computing the identification rate. In this study, an innovative peg-free hand-geometry-based user identification system using spectral properties of a minimal edge connected graph representation of hand image is proposed. The multiclass support vector machine is employed for identification of the claimed user. The geometrical information embedded in the prominent edges will contribute to achieve better identification rate. The experimentation is carried on two databases, namely GPDS150 hand database and hand images of VTU-BEC-DB multimodal database. The minimal edge connected graph with 30 prominent edges of hand image graph achieves better identification with a faster rate.

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