Research On Face Tracking Algorithm Based On Feature Classifier And Template Matching

In recent years,with the rapid development of information and the need of information,face analysis has become a hot issue in the fields of computer vision,artificial intelligence and pattern recognition.There are six kinds of subjects in face analysis,among which,face detection and face tracking are the most popular topics.It has applied widely in video surveillance,identity authentication,intelligent human-computer interaction,3D entertainment and multimedia.Face tracking is often inseparable from face detection because accurate face detection ensures accurate and stable face tracking.On the basis of face detection and template matching,this thesis propose new face tracking algorithms,improve them and fuse the with other machine learning algorithm.Extend experiments show that our algorithms ensure the accuracy,stability and effectivity of face tracking.For face tracking,one method is to use the face tracking algorithm,matching the face position according to its position at the previous frame.Otherwise this method will cause the accumulation of errors,and "template drift" phenomenon will occur.Once the face disappears,the face tracking algorithm will be invalid.What's more,the face posture transformation and difficulty of relocation after face position changes affect the tracking effect.Another method is to implement face tracking through face detection,which determines the face location at each frame.This method need to train the detector in advance,and it only tracks the positive face.Occlusion,posture change and illumination all caused tracking failure.From the above,we can see that the use of tracker or detectors individually does not track face successfully.This thesis is devoted to combine the above two methods to make them play their respective advantages in face tracking.The organization of this thesis is as follow.Chapter 1 is introduction including the related research background and research overview.Chapter 2 introduces the related technology of face tracking and face detection method based on feature classifier.It focuses on the face detection method based on the detection method of Adaboost classifier.Matching tracking algorithms are also introduced.Chapter 3 introduces the principle and process of Adaboost algorithm,and proposes a template matching tracking algorithm based on face detection.Our algorithm fuses face detection and template matching to overcome the problem of template drift and face relocation after pose transformation and disappear,which maintains the stability of the face tracking.For some problems of the algorithm in chapter 3,chapter 4 introduces the generating and matching algorithm of perceptual hash template.We improve the Adaboost algorithm in chapter 3,the training classification strategy is improved,and the updating method of adaptive learning template is adopted.Firstly,the algorithm uses improved Adaboost classifier to detect and extract the multiple features of face.Secondly,hash algorithm is used to generate hash template and hamming distance method is used to judgment the similarity.Finally,the template is used to update adaptively according to the adaptive template updating method.The experimental results show that the proposed algorithm can track the human face in the video sequence stably,accurately and real time.Chapter 5 is summary and prospect including the research contents of this thesis and proposed the direction of future study and research.