Teknik Pengenalan Pembuluh Darah Punggung Tangan Berbasis Fitur Local Binary Pattern

Teknologi biometrik untuk mengidentifikasi dan mengenal karakteristik bagian tubuh manusia yang unik dan tetap sudah banyak dilakukan. Teknik ini menjadi bagian dari sistem keamanan di berbagai bidang seperti sidik jari, palm print, wajah, dan iris. Tetapi semua teknik ini masih memiliki keterbatasan. Vena punggung tangan merupakan salah satu bagian dari sistem biometrik populer yang memiliki karakteristik tekstur yang berbeda pada setiap individu yang terletak di dalam tubuh sehingga sulit untuk ditempa ataupun dipalsukan, higienis dan nyaman. Metode yang digunakan adalah experimental atau berbasis pada eksperimen yang bersifat analisis. Tahapan rancangan yang dibangun meliputi input data citra, ekstraksi fitur, pencocokan dan pengenalan citra vena punggung tangan. Data yang digunakan dalam penelitian ini berupa citra punggung tangan tangan kiri dan kanan. Pengambilan gambar dilakukan menggunakan sebuah webcam yang telah dimodifikasi sehingga menangkap citra NIR. Hasil dari penelitian ini menghasilkan metode yang diusulkan dapat melakukan ekstraksi fitur pada citra pembuluh darah punggung tangan dengan akurasi maksimal mencapai 90% dan waktu komputasi selama 36.0 detik

Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%