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Pose Invariant Face Recognition Using DT-CWT Partitioning and KPCA

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Pose Invariant Face Recognition Using DT-CWT Partitioning and KPCA

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Abstract

In this paper the suitability of Dual Tree Complex Wavelet Transform for pose invariant Face Recognition is studied and a feature extraction frame work is proposed. This proposed framework will aid in design of Face Recognition system to address the challenging issue like Pose Variation. In contrast to the discrete wavelet Transform (DWT) the design of Dual Tree Complex Wavelet Transform is rugged to shift Invariance and poses good directional properties. These features of DT-CWT motivated to study their suitability for Face Feature Extraction, as the features of face are oriented in different directions. In this proposed frame work the Image is decomposed using DT-CWT and the features are extracted from low frequency band using Kernel Principal Component analysis (KPCA). To show the performance, the proposed method is tested on ORL Database. Satisfactory results are obtained using proposed method compared to existing state of art.

References

15 Cites in Article
  1. 1. A Patil,S Kolhe,P Patil (2009). Face Recognition by PCA Technique. Emerging Trends in Engineering and Technology (ICETET), 192-195.
  2. 2. E Kokiopoulou,Y Saad (2004). PCA and kernel PCA using polynomial filtering: a case study on face recognition. PCA and kernel PCA using polynomial filtering: a case study on face recognition
  3. 3. D Meedeniya,D Ratnaweera (2007). Enhanced face recognition through variation of Principle Component Analysis (PCA). 2007 International Conference on Industrial and Information Systems, 347-352.
  4. 4. Bruce (2009). poon1, m. ashraful amin2, hong yan, "pca based face recognition and testing criteria. Machine Learning and Cybernetics, 09, 2945-2949.
  5. 5. Utsumi Yuzuko,Iwai Yoshio,Yachida Masahiko (2006). Performance Evaluation of Face Recognition in the Wavelet Domain. Performance Evaluation of Face Recognition in the Wavelet Domain
  6. 6. H Demirel,G Anbar (2008). Pose invariant face recognition using probability distribution function in different color channels. IEEE Signal Process. Lett, 15, 537-540.
  7. 7. (2007). Jackson, Prof. Stephen Philip, (born 17 July 1962), Frederick James Quick and Cancer Research UK Professor of Biology, Department of Biochemistry, Cambridge University, since 2009 (Frederick James Quick Professor of Biology, Department of Zoology, 1995–2009); Senior Scientist, since 1996, Head, Cancer Research UK Laboratories, since 2004, Wellcome Trust/Cancer Research UK Gurdon Institute (formerly Wellcome/CRC Institute of Cancer and Developmental Biology), Cambridge University; Fellow of St John’s College, Cambridge, since 1995. The Database of Faces, formerly ORL face database
  8. 8. A Georghiades,P Belhumeur,D Kriegman (2001). From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 643-660.
  9. 9. Alaa Eleyan,Huseyin Ozkaramanli,Hasan Demirel (2009). Dual-tree and single-tree complex wavelet transform based face recognition. 2009 IEEE 17th Signal Processing and Communications Applications Conference, 536-539.
  10. 10. Guo-Yun Zhang,; Shi,Yu Peng,Hong-Min Li (2008). Combination of dual-tree complex wavelet and SVM for face recognition. International Conference on Machine Learning and Cybernetics, 5, 2815-2819.
  11. 11. G Chen,T Bui,A Krzyzak (2006). Palmprint Classification using Dual-Tree Complex Wavelets. IEEE International Conference on Image Processing, 2645-2648.
  12. 12. G Chen,W Xie (2005). Pattern recognition using dualtree complex wavelet features and SVM. Canadian Conference on Electrical and Computer Engineering, 2053-2056.
  13. 13. Yigang Peng,; Xudong Xie,Wenli Xu,Qionghai Dai (2008). Face recognition using anisotropic dual-tree complex wavelet packets. 19th International Conference on Pattern Recognition, 1-4.
  14. 14. Turgay Celik,Tardi Tjahjadi (2010). Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform. IEEE Geoscience and Remote Sensing Letters, 7(3), 554-557.
  15. 15. Shi-Qian Wu,Li-Zhen Wei,Zhi-Jun Fang,Run-Wu Li,Xiao-Qin Ye (2007). Infrared face recognition based on blood perfusion and sub-block dct in wavelet domain. 2007 International Conference on Wavelet Analysis and Pattern Recognition, 3, 1252-1256.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

K. Punnam Chandar, T. Satyasavithri. 2014. "Pose Invariant Face Recognition Using DT-CWT Partitioning and KPCA". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F5).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-F Classification I.4.8 I.7.5
Version of record
v1.2

Issue date
December 31, 2014

Language
English
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Pose Invariant Face Recognition Using DT-CWT Partitioning and KPCA

K. Chandar
K. Chandar Kakatiya University
T. Satyasavithri
T. Satyasavithri <p>Kakatiya University</p>