Dual subspace nonnegative matrix factorization for person-invariant facial expression recognition

Person-dependent appearance changes tend to increase difficulties in automatic facial expression recognition. Although one can use neutral face images to reduce the personal variations, acquisition of neutral face images may not always be possible in real cases. In order to remove the person-dependent influence from expressive images, we propose a dual subspace nonnegative matrix factorization (DSNMF) to decompose facial images into two parts: identity and expression parts. The identity part should characterize person-dependent variations, while the expression part should characterize person-invariant expression features. Our experimental results show that the proposed method significantly outperforms existing approaches on the CK+ and JAFFE expression databases.