Abstrait

Recognizing Faces Using Optimization and Feature Reduction Algorithms

Ramya Priya R, L.M.Nithya

Face recognition presents a challenging problem in the field of image analysis and computer vision. Also it received a good attention over the last few years. The applications of face recognition in the areas such as person tracking, surveillance etc. To recognize faces after plastic surgery is still difficult one. Plastic surgery procedures to enhance the facial appearance of an individual to get a younger or good look. Apart from cosmetic reasons, plastic surgery procedures are beneficial for patients suffering from several kinds of disorders caused due to excessive structural growth of facial features or skin tissues. There is a possibility of attacking others privacy by Criminals. To change facial geometry and texture of face increases the interclass variability and therefore, matching post-surgery images with pre-surgery images becomes a difficult task for automatic face recognition algorithms. Variations in pose, expression, illumination, aging and disguise are considered as major challenges in face recognition and several techniques have been proposed to address these challenges, multi-objective evolutionary algorithms are used to match face images before and after plastic surgery. In proposed system, PSO(particle swarm optimization) can be used to produce better accuracy in altered faces by using lbest and gbest methods..The work can be extended to identify misclassifications present in face by means of assuming cost for each misclassification