Dissertation > Excellent graduate degree dissertation topics show

Face Recognition Based on KPCA and Wavelet Transform

Author: ChenSong
Tutor: CaiJianLi
School: Xiamen University
Course: Control Theory and Control Engineering
Keywords: Face recognition KPCA SVM
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 221
Quote: 2
Read: Download Dissertation

Abstract


Face recognition has become an issue, which is complex, involving a wide range of applications and broad prospects as for the face’ s non-rigid and volatility, and has set off an upsurge and made breakthrough progress in recent years. Though researchers have accumulated rich results in the face recognition technology, they also have encountered some difficulties such as the effective extraction of features, the improvement of recognition rate and velocity of identification and so on. Face recognition is in relation of a lot of technologies, the two keys are extraction of features and classification methods. This paper deals with effective extraction of features of facial characteristics, improvement of the recognition rate and speed on face recognition problems , proposed a feature extraction algorithm based on feature combination, which is proved to be effective. In this paper, the specific contents and innovations include:(1) Introducing and studying the theory involved with face recognition.(2) Complete the face samples image enhancement, the normalized geometric and gray and white processing at the preprocessing time, which would be effective in improving image quality, and lower its computational complexity.(3)Based on the different characteristics of wavelet coefficients: the low-frequency part characterizes the overall image (shape), and the high-frequency part consists of a considerable number of details. Decompose the original tri-tier image by wavelet decomposition, choose the first, second and third floors of the low-frequency band as smooth wavelet characteristics. This will not only retain the overall shape of the face, also weak the local details. At the same time reduces the dimensions of face images, improves the recognition rate and speed.(4) In the area of feature extraction, the KPCA is used in the obtained wavelet characteristics in the paper,to get the three principal components’ characteristics in the feature space. The relationship between the characteristics dimension and recognition rate is reserched, and the limitations of traditional methods in feature extraction is pointed out. A new feature extraction algorithm is designed based on the features combination, that is making a partition of primary eigenvector and secondary eigenvector of the obtained three principal components’ characteristics, then combine the primary eigenvector with the secondary eigenvector to be the final classify eigenvector of each original sample. At last, input the final classify eigenvectors to the classifier for classification recognition. Experiment shows that the new algorithm is superior to the traditional method of serving one certain type of wavelet as the identifying characteristics. For the more, the recognition speed also has advantages.(5) Build SVM with Polynomial kernel function, and design the multi-class SVM in accordance with the "one-to-one" strategy.Finally, conclude the full work of the text, and point out some contents in need of further study in future.This paper emulates the proposed algorithm by MATLAB simulation, and gives detailed experimental data.

Related Dissertations

  1. Soft Sensor of Naphtha Dry Point on Support Vector Machines Regression,TE622.1
  2. The Research of the Fault Diagnoses Algorithm for the Liquid Rocket Engine Testing Bed Based on PCA-SVM,V433.9
  3. ISAR Imaging Simulation of Space Targets and Target Recognition Based on ISAR Images,TN957.52
  4. Research on Autamatic Music Structrue Analysis,TN912.3
  5. Research on Algorithms of 2D Face Template Protection,TP391.41
  6. Research on Feature Extraction and Classification of Pulse Waveform for Cholecystitis and Nephrotic Syndrome Diagnosis,TP391.41
  7. Research on Classification Method of Tongue Substance Color and Tongue Coating Color Based on SVM,TP391.41
  8. The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples,TP391.41
  9. Research on Focused Crawler Based on SVM Classification Algorithm,TP391.3
  10. Research on Predicting Intrinsic Disorder Protein Structure Based on Supervision Manifold Learning Algorithm,Q51
  11. Study on the Road Condition Monitoring Based on Vehicular 3D Acceleration Sensor,TP274
  12. Research of Orange Quality Classification Technology Based on Computer Vision,TP391.41
  13. Research of Video Face Recognition Based on Weighted Voting and Key-Frame Extraction,TP391.41
  14. Face Recognition Method Based on DE,TP391.41
  15. Analysis on Synoptic Climatology Characteristics and Forecast Methods of Fog in Hainan,P457
  16. Research of License Plate Recognition Based on Rough Sets and Fuzzy SVM,TP391.41
  17. Study on Visual Target Detection Based on SVM,TP391.41
  18. Research on Face Recognition Methods Based on Flexible Neural Tree,TP391.41
  19. Research on Face Recognition Based on AdaBoost Algorithm,TP391.41
  20. Research Onamethod for Human Face Recognition Based on MMTD,TP391.41
  21. The Research on Electrode 3D Model Classification and Retrieval Based on SVM and Shape Features,TP391.41

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