This paper presents a robust method for recognizing human faces with varying illumination as well as partial occlusion. In the proposed approach, a dual-tree complex wavelet transform (DTCWT) is employed to normalize the illumination variation in the logarithm domain. In order to minimize the variations under different lighting conditions, appropriate low frequency DTCWT subbands are truncated and the rest of the directional subbands are used to reconstruct the stable invariant face. Using the fundamental concept that patterns from a single object class lie in a linear subspace, we develop class specific dictionaries using principal component analysis (PCA) based subspace learning on illumination invariant faces. By representing the pre-processed probe image against each dictionary using l1 regularization into PCA reconstruction, target face and sparse noises are effectively factorized. Then, identification decision is made in favor of a class with minimum reconstruction error. Evaluations on challenging probe images demonstrate that the proposed method performs favorably against several state of the art methods. © 2015, Springer Science+Business Media New York.