Implementation of Face Detection System using Adaptive Boosting Algorithm

Abstract

Face detection is a very hot research topic in the fields of pattern recognition and computer vision. Its applications are widely used in artificial intelligence, surveillance video, identity authentication and human machine interaction. Face detection is based on identifying and locating a human face in the image, regardless of position, size, and condition. Various algorithms are proposed to detect faces in an image. This implementation is based on adaptive boosting algorithm and uses haar features which is based on statistical methods to detect face. Algorithm is implemented in MATLAB and synthesized by using Verilog on XILINX.

Improved side information generation algorithm based on naive Bayesian theory for distributed video coding

“…The integrated learning algorithm has been attracting more attention recently because it can create a highly accurate hypothesis by combining hypotheses created by a weak learning algorithm, AdaBoost [27][28][29][30][31][32] is one of the most promising boosting algorithms. This section describes the proposed algorithm to generate the integrated model based on the AdaBoost algorithm.…”

Real-Time Face Detection and Recognition in Complex Background

“…The Ada Boost algorithm [15] is used to extract the best features to detect the faces. The best features are chosen as weak classifiers and then concatenated to-gether as a weighted combination of these features to construct a strong classifier, which is shown in the following equation:…”