Full Description
| Cataloguing Source : | |
| ISSN : | |
| Magazine/Journal : | Makara : Teknologi |
| Volume : | Vol. 15, No. 2, November 2011: 161-167 |
| Content Type : | |
| Media Type : | |
| Carrier Type : | |
| Electronic Access : | http://journal.ui.ac.id/index.php/technology/article/viewFile/934/868 |
| Holding Company : | Universitas Indonesia |
| Location : | Direktorat Riset dan Pengabdian Masyarakat UI |
- Availability
- Digital Files: 0
- Review
- Cover
- Abstract
| Call Number | Barcode Number | Availability |
|---|---|---|
| TERSEDIA |
| No review available for this collection: 20328488 |
Abstract
Telapak tangan adalah biometrika yang masih relatif baru. Segmentasi
region of interest (ROI) dan ekstraksi fitur telapak tangan merupakan dua isu penting dalam sistem pengenalan telapak tangan. Penelitian ini memperkenalkan metode segmentasi ROI dengan titik pusat momen 2 tahap dan menerapkan metode Gabor 2D untuk menghasilkan kode
telapak tangan (palm code) sebagai fitur telapak tangan. Untuk mengukur tingkat kemiripan dua kode telapak tangan maka digunakan metode jarak Hamming ternormalisasi. Pengujian sistem dilakukan dengan menggunakan 1000 sampel telapak tangan milik 200 orang berbeda dengan 3 sampel sebagai acuan dan 2 sampel sebagai uji. Pengujian juga dilakukan dengan 2 ukuran ROI, yaitu 64 x 64 dan 128 x 128 pixel. Hasil pengujian menunjukkan bahwa unjuk kerja terbaik diperoleh ROI 64 x 64pixel mencapai 98,7% atau (FRR = 1,17%, FAR = 0,11%, T = 0,376).
Abstract
Palmprint is relatively new in physiological biometrics. Palmprint ROI segmentation and feature extraction are two important issues in palm print recognition. This paper introduces two steps in the center of mass moment method for ROI segmentation that will be applied in the Gabor 2D filter to obtain palm code as palmprint feature vector. Normalized Hamming distance was used to measure the similarity degrees of two feature vectors of palmprint. The system was tested using database 1000 palmprint images generated from 5 samples from each of the 200 persons randomly selected with ROI 64 x 64 and 128 x 128 pixel. Experiment results show that this system can achieve high performance with a success rate about 98.7% (FRR = 1.17%, FAR = 0.11%, T = 0.376) with ROI 64 x 64 pixel.
Abstract
Palmprint is relatively new in physiological biometrics. Palmprint ROI segmentation and feature extraction are two important issues in palm print recognition. This paper introduces two steps in the center of mass moment method for ROI segmentation that will be applied in the Gabor 2D filter to obtain palm code as palmprint feature vector. Normalized Hamming distance was used to measure the similarity degrees of two feature vectors of palmprint. The system was tested using database 1000 palmprint images generated from 5 samples from each of the 200 persons randomly selected with ROI 64 x 64 and 128 x 128 pixel. Experiment results show that this system can achieve high performance with a success rate about 98.7% (FRR = 1.17%, FAR = 0.11%, T = 0.376) with ROI 64 x 64 pixel.