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Hybrid Color & Frequency Method for Face Recognition

This study introduces a pioneering Hybrid Color and Frequency Features (CFF) method for enhancing face recognition performance by leveraging color information and frequency features through the Discrete Fourier Transform (DFT). The approach combines the R of RGB and IQ of YIQ to derive frequency features and employs PCA and FLD techniques to reduce dimensionality and separate pattern classes, respectively. Experimental results on the FRGC database version 2 demonstrate significant performance improvements. Future research aims to explore kernel methods for further enhancement. Thank you for your attention!

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Hybrid Color & Frequency Method for Face Recognition

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  1. A Hybrid Color and Frequency Features Methodfor Face Recognition Zhiming Liu and Chengjun Liu, IEEE

  2. Outline • Introduction • Algorithms and procedures • Experiments • Conclusion

  3. Introduction • Uncontrolled illumination • Color information and frequency features derived by means of the Discrete Fourier Transform (DFT) help improve face recognition performance. • This correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition.

  4. Introduction • RIQ : R of the RGB +IQ of the YIQ

  5. Algorithms and procedures

  6. Discrete Fourier transform of a face image Initial image R, I, Q Imaginary part Real part Magnitude Mask Algorithms and procedures

  7. Algorithms and procedures

  8. Algorithms and procedures

  9. Algorithms and procedures

  10. X :a random vector representing a frequency pattern vector. : are the eigenvectors  largest eigenvalues of X Algorithms and procedures • EFM method: • PCA reduce dimensionality • FLD separated pattern classes • PCA: Let ……..(1) (m<N)

  11. Algorithms and procedures

  12. Algorithms and procedures

  13. Algorithms and procedures • FLD: S(w) : within-class scatter matrix S(b) : between-class scatter matrix,

  14. Algorithms and procedures

  15. Algorithms and procedures

  16. Experiments • Database: FRGC version 2 Experiment 4 • the Training set contains 12 776 images that are either controlled or uncontrolled.

  17. Experiments

  18. Experiments

  19. Experiments

  20. 0.9 0.55 0.95 Experiments

  21. Experiments

  22. Experiments

  23. Conclusion • Future research will consider applying kernel methods, such as the multiclass Kernel Fisher Analysis (KFA) method presented in [9], to replace the EFM method for improving face recognition performance. • The hybrid color space improves face recognition performance.

  24. Thanks for your attention !!!

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