A novel face detection algorithm
based on PCA and Adaboost
Abstract:The principal component analysis, feature vector space is extracted to
construct weak classifier.Combined with Adaboost algorithm to construct
the strong classifier; an algorithm for face detection is presented. The
performance of the algorithm is tested based on MIT+CMU face database,
the results show that the algorithm in the running time and detection
accuracy is significantly better than the algorithm based on neural network
and support vector machine algorithm. Human face detection means that for
a given image or video, to determine whether it contains face regions, if so,
determines the number, the exact location and the size of all the faces.
Human face detection is not only a necessary precondition of face
recognition, expression recognition technology, face tracking, but also, it
plays an important role in applications like in the intelligent humancomputer interaction, video conferencing, intelligent surveillance, video
retrieval and so on. Therefore, face detection technology attracted
widespread attention in pattern recognition, computer vision, humancomputer interaction and other fields. In the field of artificial intelligence
research, face is an important biological characteristic. Face detection, face
recognition and facial expression recognition, which are the prerequisite of
achieving machine intelligence and one of the key technologies of machine
intelligence and have broad application prospects, are becoming an active
research branch.
Existing System: The development of image processing and pattern recognition
technology, face process technology has got rapid progress.
 Many algorithms of detecting faces in images have been carried out in
the past.A detailed survey of different kinds of face detection
algorithms.
 A detailed survey of different kinds of face detection algorithms was
given.A face detection scheme based on support vector machine.
 We use principal component analysis to construct the weak classifier,
combining AdaBoost algorithm.
Disadvantages: The development of image processing and pattern recognition
technology, face process technology has got rapid progress.
 The characteristics of weak classification ability, based on the
principal component analysis feature vector space is extracted to
construct weak classifier.
 Facial expression recognition research is still in its infancy, so the
theory and method remains to be improved.
Proposed System: A fast face detection based on feature extraction methods has been
proposed .Face detection based on a pipeline of simple convolution
and sub-sample modules.
 The principal component analysis feature vector space is extracted to
construct weak classifier, combined with AdaBoost algorithm.
 It is suitable for classifying data by the principal component analysis,
in order to improve the classification.
 Feature vector produced by PCA is used for face and non face feature
extraction. The first characteristic collection is projection of face and
non face vector to K' vector space.
Advantages: It construct the strong classifier an algorithm for face detection is
presented.
 A face detection scheme based on support vector machine.
 An extraction for leaf image classification by Support Vector Machine
has been proposed.
Hardware Requirements: SYSTEM
: Pentium IV 2.4 GHz
 HARD DISK
: 40 GB
 RAM
: 256 MB
Software Requirements: Operating System
: Windows 7
 IDE
: Microsoft Visual Studio 2010
 Coding Language
: C#.NET.