Abstract
Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.
Highlights
The traditional facial feature extraction [1] method is based on the geometric structure, and a standard normalized feature vector is used to describe the structural data of facial organs [2], the Euclidean distance between organs in the image is calculated to compare which two images have the highest consistency
The sparse weighted nonnegative matrix factorization (SWNMF) method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper
By adding a weight matrix in the iteration rules to improve the hierarchical expression of local features relative to the overall features and improving the iteration step size, a new sparse weighted non-negative matrix factorization (SWNMF) method based on the new iterative step size is proposed in this paper, whose recognition rate is higher than the nonnegativematrix matrixfactorization factorization (NMF) method, the sparse nonnegative matrix factorization (SNMF) [24] method, convex non-negative matrix factorization (CNMF) method [21], and multi-layer NMF method [22]
Summary
The traditional facial feature extraction [1] method is based on the geometric structure, and a standard normalized feature vector is used to describe the structural data of facial organs [2], the Euclidean distance between organs in the image is calculated to compare which two images have the highest consistency. Compared with the unconstrained matrix decomposition method, NMF has more explanatory and clear physical meaning [17] It has been widely used in image, voice, video and other non-negative datasets to extract the features [18,19]. By adding a weight matrix in the iteration rules to improve the hierarchical expression of local features relative to the overall features and improving the iteration step size, a new sparse weighted non-negative matrix factorization (SWNMF) method based on the new iterative step size is proposed in this paper, whose recognition rate is higher than the NMF method, the SNMF [24] method, CNMF method [21], and multi-layer NMF method [22]. Network) is one of the most state-of-the-art in-depth learning methods and the effectiveness of the SWNMF method is compared with the CNN method in this paper
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