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Proposes an invariant feature set for recognizing nonoccluded military vehicles in natural FLIR (forward-looking infrared) images. The proposed feature set is extracted from global and local shape information to improve recognition performance. After segmenting a target, a radial function is defined from a target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined, which designates distance between boundary points of upper region and a line drawn by two extreme points. From two functions and target boundary, four global and four local shape features are extracted. They are more invariant to similarity transform than traditional feature sets. In the experiments, we show that the proposed features are superior to the traditional feature sets with respect to invariance and recognition performance.