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Suharsono Bantun
Universitas Sembilanbelas November Kolaka
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Biometrika Nirsentuh Berbasis Pengenalan Pembuluh Darah pada Telapak Tangan Menggunakan Wavelet dan Local Line Binary Pattern Jayanti Yusmah Sari; Suharsono Bantun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.4530

Abstract

To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.
Co-Authors Abdullah Igo Baran Daiona Ahmad Ashari Andi Nur Melly Auliani Arief Budiman Aspian Achban Dimas Febriyan Priambodo Fathur Rahman Rustan Hardianti Hardianti Indar Ismail Jamaluddin Jayanti Yusmah Sari Kuntjoro Adji Sidarto La Ode Ahmad Saktianyah La Ode Hasnuddin S. Sagala Lara Sri Wahyuni Mardianto Mardianto Mardianto Mardianto Muhammad Syaiful Mutmainnah Muchtar Nirsal Nirsal Nirsal Noorhasanah Z Noorhasanah Z Nur Fajriah Muchlis Nurfitria Ningsi Phradiansah Phradiansah Qammaddin Qammaddin Rabiah Adawiyah Rahmat Karim Sarimuddin, Sarimuddin Selviani Selviani Suci Pricilia Lestari Syaban, Kharis Syahrul Syahrul Teguh Novriadi Yuwanda Purnamasari Pasrun