Genetic Optimization of Neural Networks for Person Recognition based on the Iris

Patricia Melin, Victor Herrera, Danniela Romero, Fevrier Valdez, Oscar Castillo
2012 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Abstrak Tulisan ini menjabarkan penerapan arsitektur jaringan syaraf modular untuk kegunaan identifikasi orang menggunakan citra iris mata sebagai ukuran biometrik. Database iris mata manusia diperoleh dari Institut Automasi Akademi Ilmu Pengetahuan Cina (CASIA). Hasil simulasi ditunjukkan dengan menggunakan pendekatan jaringan syaraf modular, optimasinya menggunakan algorima genetik dan penggabungannya dengan metode lain seperti metode jaringan gerbang, integrasi fuzi tipe-1 dan penggabungan
more » ... zi teroptimasi dengan algoritma genetik. Hasil simulasi menunjukkan tingkat indetifikasi yang bagus saat mengggunakan integrator fuzi dan struktur terbaik dimiliki oleh algoritma genetik. Abstract This paper describes the application of modular neural network architectures for person recognition using the human iris image as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with different methods, such as: the gating network method, type-1 fuzzy integration and optimized fuzzy integration using genetic algorithms. Simulation results show a good identification rate using fuzzy integrators and the best structure found by the genetic algorithm. 310 attest to the uniqueness of a person from far irrepressible immutable part of the body [5] . Another definition mentions that biometrics is based on the premise that each individual is unique and has distinctive physical traits or behaviors, which can be used to identify or validate [24] . Within the large field of biometrics where one can highlight, fingerprint recognition, retinal and voice, among others, we can highlight the iris recognition as a biometric tool for person recognition in a unique and highly accurate fashion This paper presents research work on integrating results of a modular neural network using the CASIA database, and obtaining the best identification when using type-1 fuzzy logic integrators developed by the genetic algorithms. Optimization of the neural networks was performed with genetic algorithms (GAs), which are essentially a method that creates a population of individuals to find the most appropriate one by simulating evolution [25] [26] [27] [28] . This process is based on natural selection by using operators such as the crossover and mutation to create new individuals. The modular neural network architectures and the chromosomes produced by the genetic algorithms with the best parameters found for the network were tested for their performance and operation, and the results of the different integrators, such as the gating network and type-1 fuzzy logic integrators, were compared for this problem. Research Method Neural networks are composed of many elements (Artificial Neurons), grouped into layers that are highly interconnected (with the synapses), which are trained to react (or give values) in a way you want to input stimuli. These systems emulate in some way, the human brain. Neural networks are required to learn to behave (Learning) and someone should be responsible for the teaching or training (Training), based on prior knowledge of the environment problem [7], [5] . A neural network is a system of parallel processors connected together as a directed graph. Schematically, each processing element (neuron) of the network is represented as a node. These connections provide a hierarchical structure trying to emulate the physiology of the brain for processing new models to solve specific problems in the real world. What is important in developing neural networks is their useful behavior by learning to recognize and apply relationships between objects and patterns of objects specific to the real world. In this respect neural networks are tools that can be used to solve difficult problems [29], [8], [30] . Artificial neural networks are inspired by the architecture of the biological nervous system, which consists of a large number of relatively simple neurons that work in parallel to facilitate rapid decision-making [24] . Fuzzy logic was proposed for the first time in the mid-sixties at the University of California Berkeley by the brilliant engineer Lotfi A. Zadeh [31], [32] . Who proposed what it's called the principle of incompatibility: "As the complexity of system increases, our ability to give precise instructions and build on their behavior decreases to a threshold beyond which the accuracy and meaning are mutually exclusive characteristics." Then introduced the concept of a fuzzy set, under which lies the idea that the elements on which to build human thinking are not numbers but linguistic labels. Fuzzy logic can represent the common knowledge as a kind of language that is mostly qualitative and not necessarily a quantity in a mathematical language by means of fuzzy set theory and the characteristic functions associated with them [32] . Fuzzy logic has gained a great reputation for the variety of applications, ranging from control of complex industrial processes to the design of artificial devices for automatic deduction, through the construction of household electronic appliances and entertainment as well as diagnostic systems [33] [34] [35] [36] [37] [38] . Fuzzy logic is an area of soft computing, which allows one computer system to the reason for the uncertainty [31] . This corresponds, in the real world, to many situations where it is difficult to decide unequivocally whether or not something belongs to a specific class [39] [40] [41] [42] . Fuzzy logic is a useful tool for modeling complex systems [43] [44] [45] [46] [47] [48] . However, it is often difficult for human experts to define the fuzzy sets and fuzzy rules used by these systems [36] . This is particularly true for type-2 fuzzy systems that use uncertain membership functions and that have recently been applied to many real-world problems [49] [50] [51] [52] [53] [54] [55] [56] [57] . Genetic algorithms were introduced by the first time by a professor of the University of Michigan named John Holland [31], [5] . A genetic algorithm, it is a mathematical highly parallel
doi:10.12928/telkomnika.v10i2.167 fatcat:bz4c2ftevzhnjaphdkomtwgwbq