Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.

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Block kernel nonnegative matrix factorization for face recognition

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691318500595 ◽

2019 ◽

Vol 17 (01) ◽

pp. 1850059

Author(s):

Wen-Sheng Chen ◽

Jingmin Liu ◽

Binbin Pan ◽

Yugao Li

Keyword(s):

Face Recognition ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Nonnegative Matrix ◽

Superior Performance ◽

Nonlinear Features ◽

Data Points ◽

Class Information ◽

Sparse Features ◽

Facial Images

Nonnegative matrix factorization (NMF) is a linear approach for extracting localized feature of facial image. However, NMF may fail to process the data points that are nonlinearly separable. The kernel extension of NMF, named kernel NMF (KNMF), can model the nonlinear relationship among data points and extract nonlinear features of facial images. KNMF is an unsupervised method, thus it does not utilize the supervision information. Moreover, the extracted features by KNMF are not sparse enough. To overcome these limitations, this paper proposes a supervised KNMF called block kernel NMF (BKNMF). A novel objective function is established by incorporating the intra-class information. The algorithm is derived by making use of the block strategy and kernel theory. Our BKNMF has some merits for face recognition, such as highly sparse features and orthogonal features from different classes. We theoretically analyze the convergence of the proposed BKNMF. Compared with some state-of-the-art methods, our BKNMF achieves superior performance in face recognition.

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Face Recognition Using Region-Based Nonnegative Matrix Factorization

Communication and Networking - Communications in Computer and Information Science ◽

10.1007/978-3-642-10844-0_73 ◽

2009 ◽

pp. 621-628 ◽

Cited By ~ 3

Author(s):

Wonmin Byeon ◽

Moongu Jeon

Keyword(s):

Face Recognition ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Nonnegative Matrix

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Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Symmetry ◽

10.3390/sym11030354 ◽

2019 ◽

Vol 11 (3) ◽

pp. 354

Author(s):

Jing Zhou

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Recognition Rate ◽

Nonnegative Matrix ◽

Facial Feature ◽

Face Database ◽

Base Matrix ◽

Sparse Nonnegative Matrix Factorization ◽

Pca Method

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.

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