원문정보
초록
영어
With the development of artificial intelligence and pattern recognition technology, more and more research related to human face is constantly developing in all walks of life. At the present stage, the traditional face recognition algorithm based on LBP and SVM is not good, and the process of feature extraction and feature classification are deeply studied in this paper. For feature extraction, the authors put forward an improved CS-LBP texture feature; for feature classification, the author uses the histogram intersection (HIK) kernel function to classify the features which has high efficiency and good effect. Subsequently, experiments are carried out on the Yale data set and the ORL data set. Experimental results show that the proposed algorithm has a significant improvement on the face recognition effect of face direction change, and the illumination change is slightly improved. In the natural environment, most face recognition has the influence of human face direction and noise, and the effect of noise is a hot direction of face recognition research in the future.
목차
1. Introduction
2. Algorithm for Feature Extraction in Face Recognition
2.1. LBP Texture and Feature Algorithm of Improved Texture
2.2. Texture Features of 2D CS-LBP
2.3. Texture Feature of Block 2D CS-LBP
3. Classification Algorithm for Face Recognition Feature
3.1. Support Vector Machine
3.2. Kernel Method
3.3. HIK Nuclear Method
4. Realize of Face Recognition System
4.1. The Process of Face Recognition System
4.2. Selection of Data Sets
4.3. Comparison of the Experimental Process
4.4. Experimental Results and Analysis
5. Conclusions
References