Facial expression recognition using lp-norm MKL multiclass-SVM
Xiao Zhang, Mohammad H. Mahoor, S. Mohammad Mavadati · 2015
Automaticrecognitionoffacialexpressionsisan interesting and challenging research topic in the field of pat- tern recognition due to applications such as human-machine interface design and developmental psychology. Designing classifiers for facial expression recognition with high relia- bility is a vital step in this research. This paper presents a novel framework for person-independent expression recog- nition by combining multiple types of facial features via multiple kernel learning (MKL) in multiclass support vector machines (SVM). Existing MKL-based approaches jointly learn the same kernel weights withl1-norm constraint for all binary classifiers, whereas our framework learns one kernel weight vector per binary classifier in the multiclass-SVM with lp-norm constraints (p ≥ 1), which considers both sparse and non-sparse kernel combinations within MKL. We studied the effect of lp-norm MKL algorithm for learning the kernel weights and empirically evaluated the recog- nition results of six basic facial expressions and neutral faces with respect to the value of p. In our experiments, we combined two popular facial feature representations, histogram of oriented gradient and local binary pattern his- togram, with two kernel functions, the heavy-tailed radial basis function and the polynomial function. Our experi- mental results on the CK+, MMI and GEMEP-FERA face databases as well as our theoretical justification show that this framework outperforms the state-of-the-art methods and