High-Order Circular Derivative Pattern for Image Representation and Recognition

Micropattern based image representation and recognition, e.g. Local Binary Pattern (LBP), has been proved successful over the past few years due to its advantages of illumination tolerance and computational efficiency. However, LBP only encodes the first-order radial-directional derivatives of spatial images and is inadequate to completely describe the discriminative features for classification. This paper proposes a new Circular Derivative Pattern (CDP) which extracts high-order derivative information of images along circular directions. We argue that the high-order circular derivatives contain more detailed and more discriminative information than the first-order LBP in terms of recognition accuracy. Experimental evaluation through face recognition on the FERET database and insect classification on the NICTA Biosecurity Dataset demonstrated the effectiveness of the proposed method.