Support Vector Machine (SVM) is a type of supervised learning algorithm that can be used for classification or regression tasks. The algorithm is used specifically for sample, high dimensional facial recognition problems. It is a classifier developed from a generalized portrait algorithm. In face recognition, we use the extracted face features and SVM to find the hyperplane for distinguishing different faces. SVM is particularly useful when the data has many features, and when there is a clear margin of separation in the data.
The main idea behind SVM is to find a hyperplane that maximally separates the different classes in the training data. This is done by finding the hyperplane that has the largest margin, which is defined as the distance between the hyperplane and the closest data points from each class. Once the hyperplane is determined, new data can be classified by determining on which side of the hyperplane it falls. SVMs are particularly useful when the data has many features, and/or when there is a clear margin of separation in the data.
SVM can be of two types:
Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier.
Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as non-linear data and classifier used is called as Non-linear SVM classifier.