Abstract
This paper presents a novel hybrid method which exploits both local and global discriminative features by a fusion for face representation and recognition. The whole face images are divided into a number of non-overlapping sub-regions to extract local discriminant features. The global discriminant features are extracted from the whole face images. The PCA and FLD methods are applied on the fused feature vector to extract lower dimensional discriminant features. The simulation results of the proposed method on the CMU PIE, FERET and AR face databases have been compared with other approaches and have demonstrated consistently improved performances.
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