"FAST FACE DETECTION USING ADABOOST"
FACE MOTION DETECTION USING ADABOOST ALGORITHM Abstract In this paper, a face detection method is presented. It is a difficult task in image analysis which has each day more and more applications. The main idea in the building of the detector is a learning algorithm based on ada-boost. The family of simple classifiers contains simple rectangular wavelets which are reminiscent of the Haar basis. Their simplicity and a new image representation called Integral Image allow a very quick computing of these Haar-like features. Then a structure in cascade is introduced in order to reject quickly the easy to classify background regions and focus on the harder to classify windows. The structure of the final classifier allows a real-time implementation of the detector. Some results on real world examples are presented. Our detector yields good detection rates with frontal faces and the method can be easily adapted to other object detection tasks by changing the contents of the training dataset. BLOCK DIAGRAM Classifier design Data acquisition Pre- (Image processing Feature capturing) (Filtering) extraction Classification EXISTING SYSTEM: The existing methods for can be divided into image based methods and feature based methods DISADVANTAGE: 1. The quality of the final detection depends highly on the consistence of the trainingset. 2. Both the size of the sets and the interclass variability are important factors to take in account PROPOSED SYSTEM: We have developed an intermediate system, using a boosting algorithm to train a classifier which is capable of processing images rapidly while having high detection rates. The main idea in the building of the detector is a learning algorithm based on boosting AdaBoost is an aggressive learning algorithm which produces a strong classifier by choosing visual features in a family of simple classifiers and combining them linearly. ADVANTAGE: 1. This improves both, the detection speed and the detection efficiency. 2. Our detector yields good detection rates with frontal faces and the method can be easily adapted to other object detection tasks by changing the contents of the training dataset. DOMAIN Digital Image Processing Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subfield of digital signal processing, digital image processing has many advantages over analog image processing; it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing. SOFTWARE REQUIREMENTS MATLAB 7.0 AND ABOVE MATLAB MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include: Math and computation Algorithm development Modeling, simulation, and prototyping Data analysis, exploration, and visualization Scientific and engineering graphics Application development, including Graphical User Interface building MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar non-interactive language such as C or FORTRAN.