Multimodal biometrics provides high performance in biometric recognition systems with respect to unibiometric systems as they offer a more universal approach, added security and better recognition accuracy. Moreover, data acquisition at the feature level brings out rich information from the traits, thus fusion of modalities at this level is desirable. In this paper we propose a novel fusion technique called non-stationary feature fusion where a new structure of interleaved matrix is constructed using local features extracted from two modalities i.e. face and palmprint images. A block based Discrete Cosine Transform (DCT) algorithm is used to construct a fused feature vector by extracting independent feature vectors from each spatial image. This fused feature vector contains nonlinear information that is used to train a Gaussian Mixture Models (GMM) based statistical model. The model provides accurate estimation of the class conditional probability density function of the fused feature vector. The proposed method produces recognition rates as high as 99.7% and 97% when tested on benchmark databases-ORL-PolyU and FERET-PolyU respectively. These rates are achieved using 23% low frequency DCT coefficients. The new technique is shown to outperform existing feature level fusion methods including methods based on matching and decision level fusion.