This document summarizes an international journal article that proposes a two-phase algorithm for face recognition in the frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The algorithm works in two phases: the first phase uses Euclidean distance to determine the K nearest neighbor training samples of a test sample. The second phase represents the test sample as a linear combination of the K nearest neighbors and classifies the sample based on which class representation has the smallest deviation from the test sample. Experimental results on FERET and ORL face databases show the two-phase algorithm based on DCT and DFT outperforms other methods like two-phase sparse representation and PCA/LDA in terms of classification accuracy.