Linear Discriminant Analysis (LDA) is a widely used technique for pattern classification. It seeks the linear projection of the data to a low dimensional subspace where the data features can be modelled with maximal discriminative power. The main computation involved in LDA is the dot product between LDA base vector and the data which is costly element-wise floating point multiplications. In this paper, we present a fast linear discriminant analysis method called binary LDA, which possesses the desirable property that the subspace projection operation can be computed very efficiently. We investigate the LDA guided non-orthogonal binary subspace method to find the binary LDA bases, each of which is a linear combination of a small number of Haar-like box functions. The proposed approach is applied to face recognition. Experiments show that the discriminative power of binary LDA is preserved and the projection computation is significantly reduced.