Using sparse regression to learn effective projections for face recognition

We explore sparse regression for effective feature selection and classification in face identity and expression recognition. We argue that sparse regression in pixel space is inappropriate. We propose instead a method which combines the virtues of sparse regression with projection methods such as PCA and FDA. The method can learn a sparse set of discriminative projections and increase recognition accuracy beyond that achievable by FDA.We demonstrate this by performance comparisons on three face data sets.