Abstract

In this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features by making use of discrete cosine transform (DCT) and singular value decomposition (SVD) and the local features by utilizing non-negative matrix factorization (NMF) are obtained. Furthermore, the feature vectors which are fused with global and local features are also obtained. In this paper, the feature vectors are used to train SVM to realize the face recognition, and the computer simulation illustrates the effectivity of this method on the ORL face database.

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