Neural-network-based system for recognition of partially occluded shapes and patterns

Dinesh P. Mital; Eam-Khwang Teoh; S. K. Amarasinghe; P. N. Suganthan

doi:10.1117/12.257278

31 October 1996 Neural-network-based system for recognition of partially occluded shapes and patterns

Dinesh P. Mital, Eam-Khwang Teoh, S. K. Amarasinghe, P. N. Suganthan

Proceedings Volume 2908, Machine Vision Applications, Architectures, and Systems Integration V; (1996) https://doi.org/10.1117/12.257278
Event: Photonics East '96, 1996, Boston, MA, United States

Abstract

The purpose of this paper is to demonstrate how a structural matching approach can be used to perfonn effective rotational invariant fingerprint identification. In this approach, each of the exiracted features is correlated with Live of its nearest neighbouring features to form a local feature gmup for a first-stage matching. After that, the feature with the highest match is used as a central feature whereby all the other features are correlated to form a global feature group for a second.stage matching. The correlation between the features is in terms of distance and relative angle. This approach actually make the matching method rotational invariant A substantial amount of testing was carried out and it shows that this matching technique is capable of matching the four basic fingerprint patterns with an average matching time of4 seconds on a 66Mhz, 486 DX personal computer.

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Dinesh P. Mital, Eam-Khwang Teoh, S. K. Amarasinghe, and P. N. Suganthan "Neural-network-based system for recognition of partially occluded shapes and patterns", Proc. SPIE 2908, Machine Vision Applications, Architectures, and Systems Integration V, (31 October 1996); https://doi.org/10.1117/12.257278

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KEYWORDS

Binary data

Data modeling

Neural networks

Computer programming

Tolerancing

Databases

Object recognition

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