In this paper we propose the discrete cosine transform (DCT) mod 2 feature set, which utilizes polynomial coefficients derived from 2D DCT coefficients obtained from spatially neighboring blocks. Face verification results on the multi-session VidTIMIT database suggest that the DCT-mod 2 feature set is superior (in terms of robustness to illumination direction changes and discrimination ability) to features extracted using three popular methods: eigenfaces principal component analysis, 2D DCT and 2D Gabor wavelets. Moreover, compared to Gabor wavelets, the DCT-mod 2 feature set is over 80 times faster to compute. Additional experiments on the Weizmann database also show that the DCT-mod 2 approach is more robust than 2D Gabor wavelets and 2D DCT coefficients.