Study of Efficient Algorithms for Face Detection: Cascaded and Parallel GSLDA
Jay Prakash Maurya, Abhilasha R Wakde · 2014
Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Face detection can be regarded as a more general case of face localization. In face localization, the task is to find the locations and sizes of a known number of faces (usually one).The work proposed in this paper is based on advance technique for face detection from using (GSLDA) i.e. Greedy Sparse Linier Discriminate Analysis. With the help of this technique images can be processed extremely rapid and high detection rates can be achieved as compare to AdaBoost(1). We have managed to organize classifiers in parallel and in cascaded manner, with critical order of grid that detects faces rapidly. The paper has research work for face detection has following steps: first is the method of feature selection the method of introducing Integral image by which new is represented which allows the features used by our detector to be computed very quickly for each classifier. The second step is learning algorithm, which is based on GSLDA which selects weak classifiers based upon the maximum class separation criterion (2, 3).