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Considering some limitations of the existing large margin classifier (LMC) and support vector machines (SVMs), this paper develops a modified linear projection classification algorithm based on the margin, termed modified large margin classifier in hidden space (MLMC). MLMC can seek a better classification hyperplane than LMC and SVMs through integrating the within-class variance into the objective function of LMC. Also, the kernel functions in MLMC are not required to satisfy the Mercer's condition. Compared with SVMs, MLMC can use more kinds of kernel functions. Experiments on the FERET face database confirm the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]