Face Identification Based on K-Nearest Neighbor
Face identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biome...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Negeri Semarang
2019-11-01
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| Series: | Scientific Journal of Informatics |
| Subjects: | |
| Online Access: | https://journal.unnes.ac.id/nju/index.php/sji/article/view/19503 |
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| author | Ni Kadek Ayu Wirdiani Praba Hridayami Ni Putu Ayu Widiari Komang Diva Rismawan Putu Bagus Candradinata I Putu Deva Jayantha |
| author_facet | Ni Kadek Ayu Wirdiani Praba Hridayami Ni Putu Ayu Widiari Komang Diva Rismawan Putu Bagus Candradinata I Putu Deva Jayantha |
| author_sort | Ni Kadek Ayu Wirdiani |
| collection | DOAJ |
| description | Face identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biometrics and accurate results. Face recognition utilizes facial features for security purposes. The classification method in this paper is K-nearest Neighbor (KNN). The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. K-NN includes an instance-based learning group. This paper developed face identification using Principal Component Analysis (PCA) or eigenface extraction methods. The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. This research is registered 30 people, each person had 3 images used for training and 2 images used for testing. The results obtained from several trials of k values are as follows. Experiments with a value of k=1 get the best accuracy, namely 81%, k=2 get 53% accuracy, and k=3 get 45% accuracy. |
| format | Article |
| id | doaj-art-df732f290fd04cd193b92cb4b10587ac |
| institution | OA Journals |
| issn | 2407-7658 |
| language | English |
| publishDate | 2019-11-01 |
| publisher | Universitas Negeri Semarang |
| record_format | Article |
| series | Scientific Journal of Informatics |
| spelling | doaj-art-df732f290fd04cd193b92cb4b10587ac2025-08-20T02:21:09ZengUniversitas Negeri SemarangScientific Journal of Informatics2407-76582019-11-016215015910.15294/sji.v6i2.195039268Face Identification Based on K-Nearest NeighborNi Kadek Ayu Wirdiani0Praba Hridayami1Ni Putu Ayu Widiari2Komang Diva Rismawan3Putu Bagus CandradinataI Putu Deva JayanthaUdayana UniversityUdayana UniversityUdayana UniversityUdayana UniversityFace identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biometrics and accurate results. Face recognition utilizes facial features for security purposes. The classification method in this paper is K-nearest Neighbor (KNN). The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. K-NN includes an instance-based learning group. This paper developed face identification using Principal Component Analysis (PCA) or eigenface extraction methods. The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. This research is registered 30 people, each person had 3 images used for training and 2 images used for testing. The results obtained from several trials of k values are as follows. Experiments with a value of k=1 get the best accuracy, namely 81%, k=2 get 53% accuracy, and k=3 get 45% accuracy.https://journal.unnes.ac.id/nju/index.php/sji/article/view/19503convolutional neural network, k-nearest neighbor, principal component analysis, haar cascade |
| spellingShingle | Ni Kadek Ayu Wirdiani Praba Hridayami Ni Putu Ayu Widiari Komang Diva Rismawan Putu Bagus Candradinata I Putu Deva Jayantha Face Identification Based on K-Nearest Neighbor Scientific Journal of Informatics convolutional neural network, k-nearest neighbor, principal component analysis, haar cascade |
| title | Face Identification Based on K-Nearest Neighbor |
| title_full | Face Identification Based on K-Nearest Neighbor |
| title_fullStr | Face Identification Based on K-Nearest Neighbor |
| title_full_unstemmed | Face Identification Based on K-Nearest Neighbor |
| title_short | Face Identification Based on K-Nearest Neighbor |
| title_sort | face identification based on k nearest neighbor |
| topic | convolutional neural network, k-nearest neighbor, principal component analysis, haar cascade |
| url | https://journal.unnes.ac.id/nju/index.php/sji/article/view/19503 |
| work_keys_str_mv | AT nikadekayuwirdiani faceidentificationbasedonknearestneighbor AT prabahridayami faceidentificationbasedonknearestneighbor AT niputuayuwidiari faceidentificationbasedonknearestneighbor AT komangdivarismawan faceidentificationbasedonknearestneighbor AT putubaguscandradinata faceidentificationbasedonknearestneighbor AT iputudevajayantha faceidentificationbasedonknearestneighbor |