دانلود مقاله تشخیص چهره سریع بر اساس نظریه فراکتال
Abstract
1- Introduction
2- Fractal coding theory and method
3- Face recognition based on fractal theory
4- Experiment results
5- Conclusions
Acknowledgments
References
Abstract
Nowadays, people are more and more concerned about accuracy, rapidity and convenience in the process of personal identification. In the field of biology and computer vision, a variety of methods have been proposed, while a proper method for face recognition is still a challenge. Although some reliable systems and advanced methods have been introduced under relatively controlled conditions, their recognition rate or speed is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. This paper proposed a fast face recognition method based on fractal theory. This method is to compress the facial images to obtain fractal codes and complete face recognition with these codes. Experimental results on Yale, FERET and CMU PIE databases demonstrate the high efficiency of our method in runtime and correct rate.
Introduction
Recently, a large number of biological features have been are applied to identity recognition, such as iris recognition, fingerprint recognition, gait recognition and face recognition. These biological features are easy to use, to distinguish and difficult to forge. Compared with other methods, non touching and aggression are the biggest advantages and features of face recognition. As a hot topic, more and more attention has been focused on the face recognition. Face recognition is considered to have broad application prospects in video surveillance, access control system, criminal investigation and other fields [1–7]. General face recognition methods can be broadly divided into two categories of local and global approaches [8]. The task of those local methods is to extract different local features. For another, global approaches process the entire image and make a general template for the face [8]. It should be noted that some deep learning methods such as Convolution Neural Network (CNN) and tensor face also achieve good results.