Dissertation > Excellent graduate degree dissertation topics show

Face recognition based on pixel change information method

Author: YaoZuo
Tutor: XuYong
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Face Recognition Feature extraction Pixel change information Interest Operators Gradient information
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 61
Quote: 0
Read: Download Dissertation


Biometric face recognition is an important branch, it is in identity verification, credit card verification, HCI and other fields have broad application prospects. Face recognition is accomplished via computer analysis of face images, extracting the effective identification information, and this information is used to identify the identity of identification. However, the face image may be exposed to light, occlusion, facial expressions and gestures, and other factors, so that it properly identify the existence of a certain degree of difficulty, therefore, to remove such negative factors as the key recognition . Face feature extraction is to determine the effect of the key. Currently used for face recognition feature extraction methods can be broadly divided into two categories: linear and nonlinear feature extraction methods feature extraction method. Common linear feature extraction methods: PCA (Principal Component Analysis), FLD (Fisher Linear Analyze), 2DPCA, 2DFLD. Nonlinear feature extraction method commonly used nuclear PCA, kernel Fisher method. These methods are widely used for face recognition, but when the face image is a complex environment, these methods often can not extract the complex environment has a strong ability to identify characteristics, resulting in low recognition rate. Interest operator is the face recognition has recently been used to solve problems, a more effective way, by its nature, the use of the image operator Interest filtration process is the extraction of pixels of the image in different directions in the process of change information . Interest-based operator at the same time face recognition method is based on pixel change information recognition method. Face Recognition Method Based Interest Operator proves the efficiency of face recognition method based on pixel change information feasibility. Gradient information of image pixel information reflects the range of change. Generally believed gradient information of image edge sensitive, not sensitive to light and other changes. Face recognition method based on gradient information can be effectively extracted texture features such as changes in illumination while alleviating the impact on the recognition of complex environment with a certain robustness. Firstly, the common linear feature extraction method, nonlinear feature extraction method, and the original Interest operator applied to face recognition. On this basis, we propose three improved Interest Operators thinking. In addition, we propose three new face recognition algorithm based on gradient information. Experimental results show that the proposed method has a complex environment is robust and can obtain a higher recognition results. Meanwhile, compared with the previous method, this method is easy to understand and master, and low computational complexity, face recognition is important.

Related Dissertations

  1. Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM,U418.6
  2. ISAR Imaging Simulation of Space Targets and Target Recognition Based on ISAR Images,TN957.52
  3. Research on Algorithms of 2D Face Template Protection,TP391.41
  4. Research on Feature Extraction and Classification of Pulse Waveform for Cholecystitis and Nephrotic Syndrome Diagnosis,TP391.41
  5. Application of Q-Learning in the Content-Based Image Retrieval Technology,TP391.41
  6. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  7. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  8. Research on Visual Measurement for Spacecraft Rendezvous and Approach,TP391.41
  9. Research on the Image Real-Time Acquisition, Storage and Image Processing System,TP391.41
  10. Feature Extraction, Selection and Combination in Lipreading,TP391.41
  11. Multi-currency Notes Technology Research and Implementation,TP391.41
  12. The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples,TP391.41
  13. Pavement Distress Recognition Based on Image,TP391.41
  14. Research on Visual Detection and Tracking of Mobile Robots,TP242.62
  15. Research on Fusion Algorithm of Hyper Spectral and High Spatial Resolution Remote Sensing Image,TP751
  16. An Approach for Identifying a Plant Resistance Gene Based on the Random Forest,Q943
  17. Tobacco Diseases Auto-Recognition Research Based on Image Processing Technology,S435.72
  18. Research on Nondestructive Detection Technology for External Qualities of Papayas Based-on Vision,S667.9
  19. Research on Identification System of Cashmere and Wool Fiber,TS101.921
  20. Research for Infrared Image Target Identification and Tracking Technology,TP391.41
  21. The Compression and Fusion Technique Research of Underwater Target Feature,TN911.7

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile