A novel robust method for outlier detection in structure and motion recovery for affine cameras is presented. It is an extension of the well-known Tomasi-Kanade factorization technique designed to handle outliers. It can also be seen as an importation of the LMedS technique or RANSAC into the factorization framework. Based on the computation of distances between subspaces, it relates closely with the subspace-based factorization methods for the perspective case presented by Sparr and others and the subspace-based factorization for affine cameras with missing data by Jacobs. Key features of the method presented here are its ability to compare different subspaces and the complete automation of the detection and elimination of outliers. Its performance and effectiveness are demonstrated by experiments involving simulated and real video sequences.