Dual Subspace Nonnegative Graph Embedding for Identity-Independent Expression Recognition

Facial expression is one of the intricate biometric traits, where different persons exhibit various appearance changes when posing the same expression. Because facial cues involved in the recognition of facial expression are not fully separate from that of facial identity, this identity-dependent behavior often complicates automatic facial expression recognition. In this paper, to address the identity-independent expression recognition problem, we propose a dual subspace nonnegative graph embedding (DSNGE) to represent expressive images using two subspaces: 1) identity subspace and 2) expression subspace. The identity subspace characterizes identity-dependent appearance variations; whereas the expression subspace characterizes identity-independent expression variations. With DSNGE, we propose to decompose each facial image into an identity part and an expression part represented by their corresponding nonnegative bases. We also address the intra-class variation issue in the expression recognition problem, and further devise a graph-embedding constraint on the expression subspace to tackle this problem. Our experimental results show that the proposed DSNGE outperforms other graph-based nonnegative factorization methods and existing expression recognition methods on CK+, JAFFE, and TFEID databases.