Factor analysis (FA) is a statistical technique similar to principal component analysis (PCA ) for explaining the variance in a data set in terms of underlying linear factors. Unlike PCA however, FA has not been widely exploited for face or object recognition. This paper explains the differences between PCA and FA, and confirms that PCA outperforms FA in a standard face recognition task. However, because FA estimates the unique variance independently for every pixel, we show that the variance estimates from FA can be used to automatically detect and suppress background pixels prior to the application of PCA, and thereby improve the performance of PCA-based object recognition systems.