NOVEL HYBRID GENETIC ALGORITHM WITH HMM BASED IRIS RECOGNITION * Prof. Dr. Ban Ahmed Mitras ** Ammar Saad Abdul-Jabbar * Dept. of Operation Research & Intelligent Techniques ** Dept. of Mathematics. College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq. Ammarsaad42@yahoo.com ABSTRACT In this paper, we proposed a novel hybrid genetic algorithm with algorithms of hidden Markov Models that is forward-backward algorithm to recognize eye iris digital image. Here genetic algorithm used to find optimal values of the hidden Markov parameters λ = (A, B, π). This proposed algorithm showed high efficiency in recognizing this type of image compared with classical hidden Markov models algorithms. Keywords: Genetic algorithm, HMM, Pattern recognition, image 1. Introduction: Recognition is a problem that has received much media attention recently with the public's heightened awareness of public security. recognition is the identification of a person's identity based on an image of their eye. This is done by analyzing the various patterns that makeup the iris. These patterns are ideal for biometric identification because they are both hard to alter as well as exceptionally complex. It has been shown that iris patterns are stable from about one year of age until death, meaning that the patterns on the iris are relatively constant over a person's lifetime. Another important feature of the iris is the great variability over many eyes. Each iris is unique (even the left and right eyes are different) and similar to fingerprints - patterns are not shared between identical twins. The biometrical personal recognition techniques require the use of expert systems, genetic algorithm, neural networks, fuzzy logic systems and the development of sophisticated computing. These methods offer the main advantage over traditional ones to be 45 able to remember and learn. For a long time, the aim of international researchers and scientists has been to create machines and systems capable of imitating certain human abilities, among which the identification based on biometric recognition or the identification through the acquisition and subsequent processing of images [1 ] [2]. The main areas of interest of biometric technologies are: Direct authentication and verification of personal identity, proof of the identity stated by the individual; Indirect identification of a person through the available biometric characteristics [6] [8]. In this paper, we proposed a new type hybrid algorithm from HMM algorithms with GA to recognize iris image 2. Genetic Algorithms (GA) Genetic algorithms are searches and optimization tools inspired by evolutionary processes. Their strength lies in their ability to
evolve near-optimal solution to complex problems, sometimes involving multiple objectives without searching the entire available space. Genetic algorithms which are pioneered by John Holland 40 years ago combine selection, crossover, and mutation operators with the goal of finding the best solution to a problem. Genetic algorithms search for this optimal solution until a specified termination criterion is met. The solution to a problem is called a chromosome. A chromosome is made up of a collection of genes which are simply the parameters to be optimized. A genetic algorithm creates an initial population (a collection of chromosomes), evaluates this population, and then evolves the population through multiple generations in the search for a good solution for the problem at hand. To reach an acceptable solution, a population must run through several generations, at each generation three processes can occur to advance the population to the next generation, these are selective crossover (recombination) and mutation, genetic algorithms follow the process which can be seen in Figure (1) [4] [5]. Figure (1): Structure of a Simple Genetic Algorithm 3. Hidden Markov Model A discrete-time Hidden Markov Model A can be viewed as a Markov model whose states cannot be explicitly observed: Each state has an associated probability distribution function, modeling the probability of emitting symbols from that state. More formally, a HMM is defined by the following entities: S = {S 1, S 2,, S N } a finite set of hidden states; the transition matrix A = {aij, 1 < j < N} representing the probability of going from state Si to state Sj with [ aij 0 and =1; the emission parameters B = {b(o Sj)}, indicating the probability of emission of the symbol o when the system state is Sj. In this paper we employ continuous HMMs: b(o Sj) is represented by a Gaussian distribution, i.e. ( ) Where N (o, ) denotes a Gaussian density of mean and covariance, evaluated at o; = { i}, the initial state probability distribution, representing probabilities of initial states, i.e. i = P[q 1 = Sj] 1 < i < N (3) with i 0 and = 1. For convenience, we denote an HMM as a triplet = (A, B, ). 46
The training of the model, given a set of sequences {Oi}, is usually performed using the standard Baum- Welch re-estimation, which determines the parameters (A, B, ) that maximize the probability P({Oi}\ ). The classical HMM contains: the training procedure is stopped after the convergence of the likelihood. The evaluation step, i.e. the computation of the probability P(O\ ), given a model and a new observation sequence O, is performed using the forward-backward procedure [3] [7]. 4. Novel Hybrid Genetic Algorithm with HMM based recognition: In this section, we introduce a proposed hybrid genetic algorithm with hidden Markov models algorithm to recognize an iris digital image. GA used to estimate (training) the parameters of HMM instead Baum-Welch algorithm. The main steps of the proposed GA- HMM algorithm introduced in the following flow chart: Figure (2): Flow Chart for the Hybrid genetic and HMM Algorithm. Star t Read tha iris image from type BMP Transform the original color iris image to gray image Segmentation the gray iris image and encoding it as O Generate initial population from HMM parameters as λ = (A, B, π) Find fitness values as log p(o/ λ using forward-backward algorithm Parents selection Crossover operation Mutation operation No Is log p(o/) bigger enough Yes recognition 5. Application on the Proposed GA- HMM Algorithm: Step (1): Read for Images: Stop The first image shows Eye1 from type BMP by size (100x100) as in figure (3). 47
The second image shows Eye11 from type JPG by size (135 175) and transforms it to type BMP as in figure (4). Figure (3) explain the inter image Eye1 (100x100 BMP) Figure (3-b) gray iris image Figure (4): explain the inter image Eye11 (135x175 JPG) Figure (3-a) color iris image Figure (4-b) gray iris image Figure (4-a) color iris image Step (2): Image Segmentation: Segmentation the iris image using clustering concept by determine high and low angles and determine (x,y) axis and encoding it as the table: Table (1): symbols of the iris 1 2 3 4 5 From these symbols we find the observation series (O1) by length (T=10000) for the first image and (O2) by length (T=23625) for the second image O1=[1;1;1;1;1;1;1;1;1;1;5;5;5;5;5;1;1;1;2;2;2;2; 5;5;5;5;1;1;1;1;5;5;5;5 ;1;1;1;1;5;5;5;5;5;3;3;3;3;3;3;3;5;5;5;5;1;1;1;1; 1;4;4;4;4;4;5;5;5;1;1;4; 4;4;4;4;4;5;5;5;1;1;1;1;1;1;1;1; ] (4) O2=[1;1;1;1;1;5;5;5;51;1;1;5;5;5;2;2;2;5;5;5;1; 1;1;2;2;2;5;5;5;3;3;3;5;5 ;5;3;3;3;3;5;5;5;4;4;4;4;4;5;5;5;5;..] (5) Step (3): Generate initial Population of HMM matrices: Using (rand) function in MATLAB to generate the initial Population of λ = (A, B, π). Step (4): Solve the evaluation problem using forward-backward algorithm: Here, we using forward backward algorithm to evaluate log p(o/ as a fitness function to genetic algorithm. 48
Step (5): Genetic Algorithm Operations: Using the steps of genetic algorithm to find max log p(o/ which is contain: a: Parents selection. b: Crossover Operation c: Mutation Operation Step (6): Stopping Criteria: If the stopping conditions satisfied then stop by means we get iris recognition. Else, go to step (4). 6. Experimental Results: A- Experimental Results on the First Image: First: Results the Proposed GA-HMM Algorithm: We applied the new (GA-HMM) algorithm on the image in figure (3) and we get: The value of log p(o/ at the final generation (18) as: ( O ) 8.326e 003 and the value of as: and A 0.0000 0.9838 0.0128 Table (2) explain the matrix at (18th) generation for the image in figure (3) 1.0000 0.0162 0.9872 0.1154 0.0071 0.1252 0.7523 Second: Results the Original HMM Algorithm: We applied the original HMM algorithm on the image in figure (3) and we get: The value of ( O ) at the final generation (37) as: ( O ) 2.8486e 003 and the value of as: and A 0.0000 0.9859 0.0118 1.0000 0.0141 0.9882 Table (3) explain the matrix at (37th ) iteration for the image in figure (3) 000055 0001.1 001153 006821 B- Experimental Results on the second Image: We applied the new (GA-HMM) algorithm on the image in figure (4) and we get: The value of ( O ) at the final generation (15) as: ( O ) 9..225e 003 and the value of as: A 0.0000 0.4572 0.9176 Table (4) explain the matrix at (15th) generation for the image in figure (4) 1.0000 0.5428 0.0824 49
0.0378 0.4387 0.0643 0.4592 Second: Results the Original HMM Algorithm: We applied the original HMM algorithm on the image in figure (4) and we get: The value of ( O ) at the final generation (34) as: ( O ) 1.7075e 003 and the value of as: 0.0000 1.0000 and 0.9918 0.0082 A 0.0040 0.9960 Table (5) explain the matrix at (34th ) iteration for the image in figure (4) 000242 0003.4 00036. 006... 7. Conclusion: We proposed a new hybrid algorithm that contain two algorithm genetic and HMM algorithms. Tables 2,3,4,5 showed that the proposed algorithm is effective compared with the original HMM algorithms (forwardbackward and Baum-Welch) algorithms because (1) Te fitness value of log p(o/ is bigger than the original HMM algorithm. (2) The number of iterations (generations) in the proposed algorithm is small compared with the number of iteration in the standard HMM algorithm. 7. References: [1] Ashbourn, J. (2000). Biometrics: Advanced Identity Verification, the Complete Guide, Springer, London [2] Berretti, S.; Del Bimbo, A. & Pala, P. (2008). SHREC'08 Entry: 3D Face Recognition using Integral Shape Information, Proceedings of IEEE International Conference on Shape Modeling and Applications, Stony Brook, New York, USA, pp. 255-256, June 2008. [3] Bicego, M. ;Castellani, U. and Murino, V., (2003) "Using Hidden Markov Models and wavelets for face recognition," in IEEE Proc. of Int. Conf on Image Analysis and Processing, pp. 52-56. [4] Koner, A., (2000), Artificial Intelligence and Soft Computing, CRC Press, Inc. [5] Lavender, B.E., (2010), Implementation of Genetic Algorithms into a Network Intrusion Detection System (netga) and integration into Nprobe, M.Sc. Thesis, California State University, Sacramento. [6] Prabhakar, S.; Pankanti, S. & Jain A. K. (2003). Biometric Recognition: Security and Privacy Concerns. IEEE Security & Privacy, pp. 33-42, March-April. [7] Rabiner, L. (1989),"A tutorial on Hidden Markov Models and selected applications in speech recognition," Proc. of IEEE 77(2), pp. 257-286. [8] Zhang, D. (2000). Automated Biometrics Technologies and Systems, Kluwer Academic Publishers, Boston (Abdul-Jabbar & Mitras, 2013) 50