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1. DUAL SUBSPACE NONNEGATIVE GRAPH EMBEDDING FOR IDENTITY-
INDEPENDENT EXPRESSION RECOGNITION
ABSTRACT
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.