Fine-grained object recognition aims at recognizing objects belonging to the same basic-level class such as dog, bird or fish, which is a challenging problem in computer vision. In this paper, we consider the problem of recognizing handbags that belong to a specific brand. In order to identify the subtle differences among handbags, we propose to enhance the handbag local structure pattern by using the Hölder exponent, and extract the feature from the enhanced handbag image to complement the feature extracted directly from the original handbag image. We term such two types of features as the complementary and original features. These features will then be fused by using Multiple Kernel Learning (MKL) for branded handbag recognition. We conduct the experiments on a newly built branded handbag dataset, the results of which demonstrate the effectiveness of the proposed complementary feature in recognizing the handbags.