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… Continue reading Support Vector Machine
Author: Nathasha K V
Haar Cascade Classifier
A Haar classifier, or a Haar cascade classifier, is a machine learning object detection program that identifies objects in an image and video. Haar Cascade classifiers are an effective way for object detection and in cases to confirm if the subject is facing straight to the camera in face recognition based applications. Haar Cascade is… Continue reading Haar Cascade Classifier
Local Binary Pattern
LBP’s simplicity, discriminative power, and computational efficiency make it a popular choice in various computer vision applications.It was first described in 1994. LBP is a type of visual descriptor that helps classify textures in images. It is a face recognition algorithm know for its performance and how it can recognize the face of a person from… Continue reading Local Binary Pattern
Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a supervised learning algorithm used for classification tasks in machine learning. LDA can handle correlation between features in the data. It is a technique used to find a linear combination of features that best separates the classes in a dataset. It is a simple and computationally efficient algorithm and can… Continue reading Linear Discriminant Analysis
Principal Component Analysis
An unsupervised learning algorithm technique called Principal Component Analysis (PCA) is used to look at how a set of variables relate to one another. PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. PCA is a dimensionality reduction technique that is reducing the number of variables… Continue reading Principal Component Analysis