Error-Correcting Output Codes (ECOC) with subclasses reveal a common way to solve multi-class classification problems. According to this approach, a multiclass problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic mutual information (FQMI) procedure. However, FQMI is not applicable on large datasets due to its high algorithmic complexity. In this paper we propose Fisher's Linear Discriminant Ratio (FLDR) as an alternative decomposition criterion which is of much less computational complexity and achieves in most experiments conducted better classification performance. Furthermore, we compare FLDR against FQMI for facial expression recognition over the Cohn-Kanade database. © 2010 IEEE.