Although the criterion-based feature extraction algorithms provided a feasible strategy to deal with the classification of high-dimensional data, most of the existing algorithms are locality-oriented and generally suffer from many issues such as uncertainty information associated with dataset and small sample size problem. In this paper, we propose a novel sparse representation-based classification method using parity symmetry strategy for face recognition. First, a subspace learning algorithm based on the geometric symmetry of face image is developed by using odd-even decomposition theorem, from which a set of parity symmetrical basis are constructed simultaneously. Second, the proposed method aims to represent a query sample as a linear combination of the most competitive training samples, and exploits an optimal representation of training samples from the classes with major relevant contributions. Experimental results conducted on ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method.