In the field of biometric recognition, similarity measure using local features such as Gabor-based coding, Local Binary Patterns (LBP) and Scale Invariant Feature Transform (SIFT) has been applied to various biometric recognition problems. These features, however, may not always exhibit higher recognition performance than the recognition algorithms of the specific biometric trait. In this paper, we propose a novel similarity measurement technique using local phase features for biometric recognition. The phase information obtained from 2D Discrete Fourier Transform (DFT) of images exhibits good performance for evaluating the similarity between images. The local phase features extracted from multi-scale image pyramids can handle nonlinear deformation of images. Through a set of experiments in some biometric recognition such as face, palmprint and finger knuckle recognition, we demonstrate the efficient performance and versatility of the proposed features compared with the state-of-the-art conventional algorithms.