An information processing system, comprising: a first local feature storing unit that stores, in association with each other, a medical article and m-number of first local features which are respectively constituted by a feature vector of i dimensions and which are generated for each of m-number of local areas including each of m-number of first feature points in an image of the medical article, using a Scale-Invariant Feature Transform, SIFT, process comprising a series of processes including feature point detection, local area acquisition, sub-area division and feature vector generation; an imaging unit; a second local feature generating unit that extracts n-number of second feature points from an image of a video captured by the imaging unit, and that generates n-number of second local features respectively constituted by a feature vector of j dimensions for n-number of local areas including each of the n-number of second feature points, using a Scale-Invariant Feature Transform, SIFT, process comprising a series of processes including feature point detection, local area acquisition, sub-area division and feature vector generation; and a recognizing unit that selects the smallest number of dimensions among the number of dimensions i of the feature vector of the first local features and the number of dimensions j of the feature vector of the second local features by comparing the number of dimensions i and the number of dimensions j, that determines a similarity between each of the m-number of first local features constituted by a feature vector up to the selected number of dimensions and each of the n-number of second local features constituted by a feature vector up to the selected number of dimensions, that counts the number of matching pairs of the first feature points and the second feature points, that determines, if a ratio of the number of the matching pairs and the number of the first feature points exceeds a threshold, whether or not the first feature points in the matching pairs can be linear-transformed into the second feature points in the matching pairs, and that recognizes that the medical article exists in the image in the video if it is determined that the first feature points in the matching pairs can be linear-transformed into the second feature points in the matching pairs. a first local feature storing unit that stores, in association with each other, a medical article and m-number of first local features which are respectively constituted by a feature vector of i dimensions and which are generated for each of m-number of local areas including each of m-number of first feature points in an image of the medical article, using a Scale-Invariant Feature Transform, SIFT, process comprising a series of processes including feature point detection, local area acquisition, sub-area division and feature vector generation; an imaging unit; a second local feature generating unit that extracts n-number of second feature points from an image of a video captured by the imaging unit, and that generates n-number of second local features respectively constituted by a feature vector of j dimensions for n-number of local areas including each of the n-number of second feature points, using a Scale-Invariant Feature Transform, SIFT, process comprising a series of processes including feature point detection, local area acquisition, sub-area division and feature vector generation; and a recognizing unit that selects the smallest number of dimensions among the number of dimensions i of the feature vector of the first local features and the number of dimensions j of the feature vector of the second local features by comparing the number of dimensions i and the number of dimensions j, that determines a similarity between each of the m-number of first local features constituted by a feature vector up to the selected number of dimensions and each of the n-number of second local features constituted by a feature vector up to the selected number of dimensions, that counts the number of matching pairs of the first feature points and the second feature points, that determines, if a ratio of the number of the matching pairs and the number of the first feature points exceeds a threshold, whether or not the first feature points in the matching pairs can be linear-transformed into the second feature points in the matching pairs, and that recognizes that the medical article exists in the image in the video if it is determined that the first feature points in the matching pairs can be linear-transformed into the second feature points in the matching pairs.