Amongst various bio-metric traits, face has been widely accepted by researchers and commercial firms. The facecan be recognized by 2-D and 3-D face recognition techniques. Face recognition has various challenges such as, occlusion, pose variations, illumination variations, and expression variations in probe and gallery faces. In this paper challenges due to illumination variations are addressed using shearlet based face recognition method. Illumination invariant face recognition is achieved with the use of logarithmic transformation and is followed by forward shearlet transform. The 2-D discrete shearlet transform provides high performance and computational efficiency than the discrete wavelet transform (DWT), in multi-directional transform domain. In order to remove illumination variations, low frequency approximate component of shearlet coefficients have made null. Subsequently the inverse shearlet transform is computed which results in a spatial domain image. Principal Component Analysis (PCA) feature extraction technique has used to extract features. The proposed technique is experimented on the Extended Yale B face database. © 2016 IEEE.