Texture representation in AAM using Gabor wavelet and local binary patterns

Abstract

Active appearance model (AAM) has been widely used for modeling the shape and the texture of deformable objects and matching new ones effectively. The traditional AAM consists of two parts, shape model and texture model. In the texture model, for the sake of simplicity, the image intensity is usually employed to represent the texture information. However, the intensity is easy to be interfered by the external environment change, e.g. illumination variations, which results in an unsatisfied model fitting. To this purpose, we present a new texture representation in AAM, which combines Gabor wavelet and local binary patterns (LBP) operator. On the one hand, Gabor wavelet can encode multi-scale and multi-direction information of an image. On the other hand, LBP is able to efficiently encode local information and compress the redundancy in the Gabor filtered images. Since the new texture representation can express an object more sophisticatedly, it will improve the accuracy of the model fitting. The experimental results on various datasets demonstrate the effectiveness of the proposed texture representation, which results in a more accurate and reliable matching between the model and new images.