In this paper, we propose a novel local steerable phase (LSP) feature extracted from the face image using steerable filter for face representation and recognition. Steerable filter is a kind of oriented filters. It is rotated very efficiently by taking a suitable linear combination of basis filters and allows adaptive control over phase as well as orientation. Phase information provided by steerable filter is locally stable with respect to scale, noise and brightness changes. Furthermore, steerable filter is implemented within a Gaussian pyramid to make use of discriminative power in the scale-space of face images. Each face is represented as multiple "steerablefaces" of different scales and orientations. With simple down-sampling, all the steerablefaces are concatenated to an augmented feature vector for evaluating similarity between face images. A nearest-neighbor classifier based on local weighted phase-correlation is used for final decision rule. Experimental results on FERET and XM2VTS databases demonstrate the performance of the proposed method.