Face recognition with positive and negative samples using support vector machine

One of the major problems in the face classification comes from a large image variance caused by unknown pose of the recognized face. A huge amount of real-world applications for face detection exist. Modern-day work even proposes that any specific detectors can be approached by means of fast detection classifiers. In this research work, support vector machine (SVM) algorithm detects face from the input image with less amount of false detection rate. This algorithm uses the concept of recognizing edges, color skin and extracting features from the face. The result is maintained by the parameters describing the parts of the face. The research implements the highly powerful concept of SVM that is used for the classification of images. This classification is based on the training data set and test data set with positive and negative samples.