Image Processing Based Gender Classification and Face Identification Using SVM

Abstract

Face Identification and Gender Classification using Image Processing and SVM is the association and streamlining of face identification model. The primary goal of this project is to bring together and optimize an automatic face detection and recognition system. Human identification refers to the classification of gender, which can improve the accuracy of identification. As a result, accurate gender classification algorithms may improve application accuracy while also reducing complexity. However, several obstacles exist for particular purposes, such as rotation as well as gray scale variations, which could also limit the application's accuracy. The main overall goal of this study is to study how to use Open CV to interpret the values in image, pattern, and array processing in addition to developing pipe-lining and SVM models. The essential target of building this module is to grasp the characteristics in picture, model, and bunch planning with Open-CV for amazing taking care of appearances for building pipe-lining, SVM models.