Identiti seseorang dapat dikenali dengan menganalisa pengenalan biometrik seseorang. Iris mata merupakan salah satu biometrik yang digunakan secara meluas dalam bidang keselamatan kerana keunikannya. Corak binari tempatan merupakan salah satu kaedah pengekstrakan ciri iris yang paling berguna. Selain itu, pengelasan K-jiran terdekat (K-NN) adalah salah satu kaedah klasifikasi digunakan secara meluas kerana mudah dipakai. Dalam projek ini, satu kaedah pengekstrakan ciri yang baru iaitu corak binari tempatan satu dimensi (1D-LBP) bergabung dengan pengelasan K-NN. Raspberry Pi 3 digunakan untuk melaksanakan sistem tersebut. Terdapat lapan subjek yang berbeza digunakan dalam sistem klasifikasi ini dan tujuh imej iris dalam setiap subjek. Terdapat dua peringkat dalam pembangunan sistem ini. Pertamanya, algoritma 1D-LBP digunakan untuk mengekstrak ciri-ciri iris dan maklumatnya dicatatkan dalam format teks. Kedua, pengelasan K-NN digunakan untuk mengelaskan maklumat-maklumat tersebut. Dua kaedah digunakan untuk menilai ciri-ciri tersebut, iaitu satu lawan satu dan satu lawan banyak. Dua puluh lapan pasang yang diklasifikasikan oleh kaedah satu lawan satu mencapai 100% ketepatan. Terdapat tujuh kombinasi diklasifikasikan dengan kaedah satu lawan banyak. Prestasi terbaik apabila tiga kelas terlibat dalam kaedah tersebut, iaitu 100% ketepatan. Kaedah satu lawan banyak mencapai ketepatan yang lebih rendah apabila bilangan subjek meningkat. Prestasi kaedah tersebut dipengaruhi oleh maklumat-maklumat iris dan nilai K yang digunakan dalam pengelasan K-NN. Kesimpulannya, kaedah 1D-LBP berjaya direalisasikan untuk iris klasifikasi.
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The identity of a person can be identified by analyzing biometric identification. Iris is one of the biometric that widely used in the field of security due to its uniqueness. There are a lot of feature extraction methods and classification methods for iris classification. Classic local binary pattern (LBP) is one of the most useful feature extraction methods. Moreover, K-Nearest Neighbour (K-NN) classifier is one of the widely use classifier due to its simplicity. Due to the current methods in feature extraction are still improving, this project proposed a new feature extraction method to increase the performance of iris classification. In this project, a classification system is proposed with the one-dimensional local binary pattern algorithm (1D-LBP) with the K-Nearest Neighbour (K-NN) classifier and the system is developed by using a Raspberry Pi 3. There are eight different subjects used to classify in this classification system and each subject consists of seven samples of normalized iris image as input to the system. There are two stages in the proposed classification system. Firstly, the 1D-LBP algorithm is used to extract the features of the normalized iris images and save the data in a text file according to the subject and the combinations to evaluate for the next stage. Secondly, the K-NN classifier is used to classify the 1D-LBP based features from the first stage. There are two methods to evaluate the features, which are one versus one and one versus many. Twenty-eight pairs of subjects are saved in different text files and classified under one versus one method. There are twenty pairs of the subjects are achieved 100% of classification accuracy. There are seven combinations of the subjects are classified by using the one versus many method. The best performance of the one versus many is when the data cluster involves three classes. The accuracy is 100%. The classification accuracy is decreased when the number of subjects in the test data is increased. The performance of the one versus many classification is affected by the 1D-LBP based information and the value of K in K-NN classifier. In conclusion, the 1D-LBP algorithm is performance well with K-NN classifier.
Realization of the 1d local binary pattern (lbp) algorithm in raspberry pi for iris classification using k-nn classifier / Siow Shien Loong