Abstrak PENDAHULUANSistem biometrika merupakan teknologi pengenalan diri menggunakan bagian tubuh manusia seperti sidik jari, telinga, wajah, geometri tangan, telapak tangan, retina, gigi dan bibir. Pengenalan wajah merupakan sistem biometrika yang banyak digunakan untuk identifikasi personal pada penggunaan mesin absensi atau akses control. Hal ini karena wajah merupakan salah satu biometrika yang paling umum digunakan untuk mengenali seseorang.Selain itu, pengenalan wajah juga tidak mengganggu kenyamanan seseorang saat pengambilan citra.Banyak orang yang mencoba untuk membangun program aplikasi pengenalan wajah dengan berbagai macam metode yang masing-masing memiliki kelebihan dan kelemahan. Ada metode yang cepat dalam mengenali citra wajah akan tetapi mengorbankan tingkat keakuratan dalam pengenalan. Ada dua hal yang menjadi masalah utama pada pengenalan wajah yaitu proses ekstraksi fitur dari citra wajah dan juga teknik klasifikasi yang digunakan untuk mengklasifikasikan wajah yang ingin dikenali berdasarkan fitur-fitur yang telah dipilih.
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Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.
In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris). Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP) method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP), a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI) detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed
The Indonesian Natural Sign System (SIBI) is one of the most natural languages of communication, especially for deaf and speech impaired. Deaf and speech impaired can understand and communicate with each other by using sign language, but some normal people will have difficulty understanding sign language with deaf and speech impunity to say. To overcome these problems need develop a system that is able to recognize the Indonesian Sign System (SIBI) which is expected capable of learning media in communicating between the deaf and normal humans. The introduction of the Indonesian Sign System (SIBI) will consists of three main stages: image acquisition, preprocessing and recognition. In this research the classification method used is Fuzzy KNearest Neighbor (FKNN) method. Based on the results of experiments conducted with the classification using the method Fuzzy K-Nearest Neighbor (FKNN) obtained an accuracy of 88%. Index Term— Fuzzy K-Nearest Neighbor, Sistem Isyarat Bahasa Indonesia (SIBI).
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