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Proceedings Paper

Military personnel recognition system using texture, colour, and SURF features
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Paper Abstract

This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

Paper Details

Date Published: 13 June 2014
PDF: 9 pages
Proc. SPIE 9090, Automatic Target Recognition XXIV, 90900Q (13 June 2014); doi: 10.1117/12.2048818
Show Author Affiliations
Martins E. Irhebhude, Loughborough Univ. (United Kingdom)
Eran A. Edirisinghe, Loughborough Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 9090:
Automatic Target Recognition XXIV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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