This paper proposes a novel method based on Haar wavelet transform and uniform local binary patterns (ULBPs) to recognize ear images. Firstly, ear images are decomposed by Haar wavelet transform. Then ULBPs are combined simultaneously with block-based and multi-resolution methods to describe together the texture features of ear sub-images transformed by Haar wavelet. Finally, the texture features are classified by the nearest neighbor method. Experimental results show that Haar wavelet transform can boost effectively up intensity information of texture unit. It is not only fast but also robust to use ULBPs to extract texture features. The recognition rates of the method proposed by this paper outperform remarkably those of the classic PCA or KPCA especially when combining block-based and multi-resolution methods.
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