Person reidentification is to identify the persons observed in nonoverlapping camera networks. Most existing methods usually extract features from the red, green, and blue color channels of images individually. They, however, neglect the connections between each color component in the image. To overcome this problem, a novel quaternionic local binary pattern (QLBP) is proposed for person reidentification in this paper. In the proposed QLBP, each pixel in a color image is represented by a quaternion so that we can handle all color components in a holistic way. A novel pseudo-rotation of quaternion (PRQ) is proposed to rank two quaternions. Some properties of PRQ are also discussed. After a QLBP coding, the local histograms are extracted and used as features. Experiments on two public benchmarking datasets, ETHZ and i-LIDS MCTS, are carried out to evaluate the QLBP performance. Comparison results show that the QLBP outperforms several stat-of-art methods for person reidentification.