“Dynamically Visual Learning for Person Identification with Sparsely Distributed Multiple Cameras”

This paper proposes a dynamical visual learning method, which aims to identify person using multiple surveillance cameras sparsely distributed in space. In the proposed method, densely distributed multiple images are captured by interpolating the object' s appearance in the sparsely distributed multiple images with a simple 3D face model, and generate two initial eigenspaces (an eigenspace for pose estimation and that for identification). In case another image is captured, the object's pose and name are estimated using the eigenspaces. The image is projected onto the 3D face model as texture information to improve the object's appearance, and the eigenspaces are regenerated. The discernment capability for person identification of the proposed method is shown by experimental results.