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.