Face Detection Using Boosting Algorithm And Ipsonet
Face Detection is a challenging research field in computer vision and is of great interest, as it is very first step in
face processing system. In this paper, we introduced a popular learning method for solving classification problems i.e.
AdaBoost, which combines number of weak classifiers into a strong classifier . By taking AdaBoost classifiers in cascaded
manner, a new boosting algorithm i.e. MLPBoost which we used as a strong classifier for face detection . These Boosting
algorithms are required to increase the speed and Accuracy of the detector, which is our prime goal. PCA i.e. Principal
Component Analysis is a popular unsupervised statistical method to find useful image representation. This method finds set
of basic images and represents faces as a linear combination of those images. . PCA is an efficient feature extraction method
that we applied to improve an accuracy of detector. Another algorithm IPSONet i.e. Improved Particle Swarm Optimization
 is used to reduce false positive images obtained after applying Boosting Algorithm. IPSONet is a training technique for
neural networks like MLP i.e. multilayer perceptron that uses an improved PSO i.e. Particle Swarm Optimization to evolve
simultaneously structure and weights of neural networks.
Index Terms- AdaBoost, Face Detection, Particle Swarm Optimization, Principal Component Analysis.