In this paper, an approach for face recognition combining multi-resolution analysis and transform domain analysis is proposed. Face Recognition system find its use in many applications such as authentication, surveillance, human-computer interaction systems etc. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In the proposed ComGW-DCT approach, features are extracted using a combination of Gabor filters and Discrete Cosine Transform (DCT). The normalised input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. Further DCT technique is adapted to reduce the feature space dimension. DCT extracts low frequency components of the Gabor wavelet thus resulting in the compression of Gabor features. For classification, k-Nearest Neighbour (k-NN) classifier is used to recognise the test image by comparing with each of the training set features. The ComGW-DCT approach is robust against illumination conditions as the Gabor features are illumination invariant. This algorithm also aims at better recognition rate using less number of features for varying expressions without affecting the computation time. The results of the proposed system are evaluated using AT&T database and MIT-India face database. © 2014 IEEE.