The Application Of Genetic Algorithm In Face Recognition

Human face recognition is a challenging issue. The traditional methods of face recognition need most heavy prepare works and many confinements in the real face recognition phase. So in this article, GA is applied in the principal steps of face recognition, including segmenting face, location of face and curing of face angle, simultaneously, the mathematical model is built.The concrete means is to design chromosome by a binary chain comprised by 8 genes, which represent a segmenting threshold that is corresponding to a gray. Selection operator is designed to be 'elite selection'; in crossover phase, 'preferred selection' is presented; differed from traditional methods, in the course of mutation, at least both a '0' and a T exist simultaneously on the same gene position in the current population and that avoid premature mostly. Inversion operator is presented to generate a new binary chain by randomly selecting two gene positions and inversing the chain between the two positions in a chromosome.According to the genetic algorithm presented in this article, amathematical description is presented and an accurate markov model is built. Based on this, the presented algorithm in this article is proved to be global convergent.The emulation illustrates that alongside the standard genetic algorithm, the convergence velocity and optimal solution of the presented genetic algorithm is enhanced mostly. Heavy works in steps of face recognition is so reduced that the effect and rate of face recognition is improved mostly.