Study on Identification of Face in Versatile Circumstances through Genetic and Ant Colony Optimization Algorithms

Abstract

Background/Objectives: Image pre-processing is done for enhancing the features of the input image for efficient image recognition. Image enhancement is done in a series of steps by employing different filtering techniques. Those steps are discussed as follows: Histogram equalization and Normalization. Methods/Statistical Analysis: Research on Identification of Face in a Multifaceted Condition by clustering Enhanced Genetic and Ant Colony Optimization Algorithms. Results: The edge detection of the clipped face image. There are a number of different face recognizing and identification method which varies on different grounds and are employed for different scenarios. Those methods are employed in the system for which FIS is developed and their results are compared. Conclusion/Application: This Face Identification System by clustering Genetic and Ant Colony Optimization algorithm shows the maximum efficiency of 96%. This ACOGA competence can be greater than before by using better face scanner, best technique of scaling and well organized technique of edge detection and feature extraction of the face image. The problems faced while working on this research work are the difficulties in detecting faces of overlapping face images and detecting different face poses.