Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition
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, fi...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/928051 |
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| _version_ | 1832559301858689024 |
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| author | Zhe-Zhou Yu Yu-Hao Liu Bin Li Shu-Chao Pang Cheng-Cheng Jia |
| author_facet | Zhe-Zhou Yu Yu-Hao Liu Bin Li Shu-Chao Pang Cheng-Cheng Jia |
| author_sort | Zhe-Zhou Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ab00506b6f8e40ceb9c0e2c7589755b0 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-ab00506b6f8e40ceb9c0e2c7589755b02025-02-03T01:30:27ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/928051928051Incremental Graph Regulated Nonnegative Matrix Factorization for Face RecognitionZhe-Zhou Yu0Yu-Hao Liu1Bin Li2Shu-Chao Pang3Cheng-Cheng Jia4College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaIn 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.http://dx.doi.org/10.1155/2014/928051 |
| spellingShingle | Zhe-Zhou Yu Yu-Hao Liu Bin Li Shu-Chao Pang Cheng-Cheng Jia Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition Journal of Applied Mathematics |
| title | Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition |
| title_full | Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition |
| title_fullStr | Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition |
| title_full_unstemmed | Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition |
| title_short | Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition |
| title_sort | incremental graph regulated nonnegative matrix factorization for face recognition |
| url | http://dx.doi.org/10.1155/2014/928051 |
| work_keys_str_mv | AT zhezhouyu incrementalgraphregulatednonnegativematrixfactorizationforfacerecognition AT yuhaoliu incrementalgraphregulatednonnegativematrixfactorizationforfacerecognition AT binli incrementalgraphregulatednonnegativematrixfactorizationforfacerecognition AT shuchaopang incrementalgraphregulatednonnegativematrixfactorizationforfacerecognition AT chengchengjia incrementalgraphregulatednonnegativematrixfactorizationforfacerecognition |