Face Recognition And It's Implementation Based On Genetic Algorithms

Owing to its potential and extensive applications, human face recognition technology has been developed rapidly in the passed decades. Because human face is nonrigid and its expression is changeful, human face recognition has been facing tremendous difficulties in actual applications, which makes human face recognition become a challenging issue. In this article, GAs are applied in the principal steps of face recognition, including image segmentation, location of face and curing the angle, and a mathematical model is built.The concrete means is to take maximum variance between two classes in Ostu method and total entropy in KSW method as fitness in image segmentation;in the detection and location of face, a mathematical model is established to measure the existence of two eyes, nose and mouse in a rectangle area, and weight-based sum of these measurements is adopted as GA's fitness. In the design of GAs' operators and performing strategies, selection operator is designed to be 'elite selection';multi-point crossover can perform between father generation and son generation;mutation requires two chromosomes, thus premature is avoided by the greatest extent. In addition, an inversion operator is given to generate a new binary string by choosing randomly two gene positions and inversing the substring between the two positions in a chromosome.For the GAs given in this article, a mathematical description is presented and the GAs' convergence properties are analyzed based on GA probability convergence theory and Markov chain theory, and the proposed algorithms are proved to be global convergent.The emulation illustrates that the convergence velocity, optimal solution of the presented genetic algorithms and the rate of face recognition, compared with the standard genetic algorithm, are enhanced significantly.