Using sparse regression to learn effective projections for face recognition
Yongxin Taylor Xi, Peter J. Ramadge · 2009
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.