IJCEM International Journal of Computational Engineering & Management, Vol. 15 Issue 2, March 2012 ISSN (Online): 2230-7893 www.IJCEM.org IJCEM www.ijcem.org 58 Time bound Adaptive Genetic Algorithm based face recognition S. N. Palod 1 , S. K. Shriwastava 2 , P. K. Purohit 3 1 Smt. Radhikatai Pandav College of Engineering, Nagpur, India 2 Shri Balaji Institute of Technology & Management, Baitul (M.P), India 3 National Institute of Technical Teachers Training & Research, Shamla Hills, Bhopal, India Abstract When huge face database has to be searched for face detection time becomes the deciding factor for certain applications such as airport security checks where face detection within a short span of time is desirable. Genetic algorithm helps in close match in a very large face database based on heuristic search. PCA based features is a compulsion from the point of view of low dimension and space limitation. This paper proposes time bound adaptive genetic algorithm so as to reduce time for face detection application in shortest span of time. Integrated expert system will still reduce time for recognition. Keywords : Genetic Algorithm, PCA, ICA. I. INTRODUCTION Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. The genetic algorithm was introduced by Regensburg in 1990. (GAs) is a class of optimization procedures inspired by the mechanisms of natural selection [1, 2]. (GAs) operates iteratively on a population of structures, each of which represents a candidate solution to the problem, encoded as a string of symbols (chromosome). A randomly generated set of such strings forms the initial population from which the (GAs) starts its search. Three basic genetic operators guide this search: selection, crossover and mutation. The basic concept behind Genetic & Evolutionary Computation (GEC) is to find an optimal (or near optimal) solution for a specific problem [5, 6, 7]. First, a number of individuals or candidate solutions are generated to form an initial population. Each individual is then evaluated and assigned a fitness obtained from the evaluation function specific to the problem at hand. Parents are then selected and new individuals are produced from the selected parents by the processes of reproduction. Survivors are selected from the previous generation and combined with the offspring to form the next generation. This process continues for user specified number of cycles.. PCA has been called one of the most valuable results from applied linear algebra. is used abundantly in all forms of analysis (from neuroscience to computer graphics) because it is a simple, nonparametric method of extracting relevant information from confusing datasets. With minimal additional effort, PCA provides a roadmap for how to reduce a complex dataset to a lower dimension to reveal the sometimes hidden, simplified structure that often underlie it [3]. II. Genetic Algorithm methodology GA is a powerful search and optimization algorithm, which are based on the theory of natural evolution. In GA, each solution for the problem is called a chromosome and consists of a linear list of codes. The GA sets up a group of imaginary lives having a string of codes for a chromosome on the computer. The GA evolves the group of imaginary lives (referred to as population), and gets and almost optimum solution for the problem. The GA uses three basic operators to evolve the population: selection, crossover, and mutation. Genetic algorithm was developed by John Holland- University of Michigan (1970s) to provide efficient techniques for optimization and machine learning applications through application of the principles of evolutionary biology to computer science. It uses a directed search algorithms based on the mechanics of biological evolution such as inheritance, mutation, natural selection, and recombination (or crossover). It is a heuristic method that uses the idea of survival of the fittest. In the genetic algorithm, the problem to be solved is represented by a list of parameters which can be used to