Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition
Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes ve...
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| Format: | Article |
| Language: | English |
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Universitas Negeri Semarang
2019-05-01
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| Series: | Scientific Journal of Informatics |
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| Online Access: | https://journal.unnes.ac.id/nju/index.php/sji/article/view/18553 |
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| author | Sri Sutarti Anggyi Trisnawan Putra Endang Sugiharti |
| author_facet | Sri Sutarti Anggyi Trisnawan Putra Endang Sugiharti |
| author_sort | Sri Sutarti |
| collection | DOAJ |
| description | Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater. |
| format | Article |
| id | doaj-art-193a849f786241b2a3b44cf1849adb5f |
| institution | OA Journals |
| issn | 2407-7658 |
| language | English |
| publishDate | 2019-05-01 |
| publisher | Universitas Negeri Semarang |
| record_format | Article |
| series | Scientific Journal of Informatics |
| spelling | doaj-art-193a849f786241b2a3b44cf1849adb5f2025-08-20T01:48:09ZengUniversitas Negeri SemarangScientific Journal of Informatics2407-76582019-05-0161647210.15294/sji.v6i1.185538766Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image RecognitionSri Sutarti0Anggyi Trisnawan Putra1Endang Sugiharti2Universitas Negeri SemarangUniversitas Negeri SemarangUniversitas Negeri SemarangFace recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.https://journal.unnes.ac.id/nju/index.php/sji/article/view/18553face recognition, feature extraction, pca, 2dpca, k-nearest neighbor. |
| spellingShingle | Sri Sutarti Anggyi Trisnawan Putra Endang Sugiharti Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition Scientific Journal of Informatics face recognition, feature extraction, pca, 2dpca, k-nearest neighbor. |
| title | Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition |
| title_full | Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition |
| title_fullStr | Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition |
| title_full_unstemmed | Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition |
| title_short | Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition |
| title_sort | comparison of pca and 2dpca accuracy with k nearest neighbor classification in face image recognition |
| topic | face recognition, feature extraction, pca, 2dpca, k-nearest neighbor. |
| url | https://journal.unnes.ac.id/nju/index.php/sji/article/view/18553 |
| work_keys_str_mv | AT srisutarti comparisonofpcaand2dpcaaccuracywithknearestneighborclassificationinfaceimagerecognition AT anggyitrisnawanputra comparisonofpcaand2dpcaaccuracywithknearestneighborclassificationinfaceimagerecognition AT endangsugiharti comparisonofpcaand2dpcaaccuracywithknearestneighborclassificationinfaceimagerecognition |