PENGENALAN CIRI GARIS TELAPAK TANGAN MENGGUNAKAN DETEKSI TEPI CANNY, EKSTRAKSI FITUR GLCM DAN KLASIFIKASI DENGAN METODE K-NN
| Main Author: | Utari, Rahayu |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
http://eprints.unram.ac.id/15603/1/skripsi.pdf http://eprints.unram.ac.id/15603/ |
Daftar Isi:
- Palm print is one of the organ of human body that can be used as identification. Palm print has a unique characteristic, hard to forge and more reliable than finger print, and identify a person using palm print as any other biometrics. In this research we proposed palm print identification using K-Nearest Neighbor (KNN). The identification consisted of data acquisition, intensity normalization result of image pre-processing segmentation, feature extraction and classification. The proposed method trained and evaluated using 900 images of 10 individuals with percentage 70:30 containing images sizes 32×64, 64×128 and 128×256. Pre-processing using canny edge detection also prosed to reduce the noise in the images. The features including contrast, energy, entropy and homogeneity on 900 extracted using Gray level co-occurrence matrix (GLCM). Finally result of pre-processing is classified using KNN with k parameter that compute distance of neighbours (similar data) between 3, 5, and 7. The evaluation resulted the best number of k for this dataset is 7 which is the highest number. Image size 32×64 give the fastest time in the extraction and classification process, which is 47.04 seconds. The accuracy of KNN classifier evaluated using k fold cross validation and archived 98% of accuracy as higher accuracy with k=7. In the future the classification need to re-evaluated using higher number of k and more variety of image data set .