dt-cwt subband partitioning for fac recognition

DT-CWT SUBBAND PARTITIONING FOR FAC RECOGNITION

K. Punnam Chandar1, T. Satya Savithri2

1Kakatiya UniversityDept. of E.C.E

Warangal, INDIA.

© ICIIS 2014

2JNTUDept. of E.C.E

Hyderabad, INDIA.

Outline

• Motivation• The Basic Problem that we studied• Previous Work

• Multi Scale Partitioning• Real Wavelets• Complex Wavelets

• Our Contribution & Results• OneS Representation• Results

• Conclusion

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MotivationThe Basic Problem that we studied

• Principal Component Analysis [Eigen Faces]– PCA is a Statistical Method.– PCA extracts the featues from the low

frequency content of the face image.– Perform Face recongnition.– This statistical Method de-emphasizes the

high frequency information, available to improve the recognition performace [1-2].

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MotivationThe Basic Problem that we studied

• Linear Discriminant Analysis [Fisher Faces]– LDA finds a linear mapping M that

maximizes the Linear Class seperability in the low-dimensional representation of the data.

– LDA is Suscepetible to over fitting the training data [3].

– Fisher Face approach is more senstitive to pose variation & variation in illumination.

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Multi Resolution Wavelet AnalysisPrevious Work

• Discrete Wavelet Transform− J. T. Chien et al. Proposed Discriminant Wavelet faces.− C. J. Chen and J. S. Zhang, Proposed Wavelet Energy as

new feature vector.• Dual Tree Complex Wavelet Transform:− Y. H. Sun and M. H. Du, DT-CWT feature combined

with onpp for face recognition.− G. Y. Zhang et al., “Combination of dual tree complex

wavelet transform and SVM for face recognition.

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Multi Resolution Wavelet Analysis

• Our Previous Work:− K. Punnam Chandar et al. Suitability of Complex

Wavelets towards face recognition,− Highest recognition rate reported (rank-1) 84%.

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Multiscale PartitioningReal Wavelets

• Real Wavelets Partition the image into four sub bands, Low-Low, Low-High, High-Low, High-High.

• Inspite of the compact support and efficient computation the DWT suffers from four fundamental, interwined shortcomings, oscillations, shift variance,aliasing and directilnality.

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Scale-one partition using DWT (db2).

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Multiscale PartitioningComplex Wavelets

• Complex Wavelets on the other hand partition the image into eight sub bands.

• The short comings of the real wavelets are overcomed.

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Scale-One Partitioning of ORL Database Face Image using DT-CWT.

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Our ContributionOnes Representation

• The scale-1 partitioning of face image using DT-CWT results in six complex coefficient high frequency sub bands oriented in directions -75, - 45, -15, 15, 45, 75 and two low frequency complex sub bands Low-Low (LL1), Low-Low (LL2).

• The scale one complex sub band coefficients magnitudes of each sub band are normalized to zero mean and unit Variance.

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Our ContributionOnes Representation

• The resultant magnitudes of the sub bands coefficients are arranged from low frequency to high frequency as [LL1, LL2, -15, 15, -45, 45, -75, 75]T in to [(8xM/2xN/2), 1] vector, we call this vector as one scale (OneS) Representation.

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Face Recognition Results on ORL Database

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NAME EERVERIFICATION RATE AT

1% FAR 0.1% FAR 0.01 % FAR

CWT_ONES 3.58% 92.14% 80.71% 67.86%

DB2_ONES 8.95% 74.64% 52.14% 39.29%

COIF2_ONES 7.85% 73.21% 45.36% 28.21%

PCA 5.70% 83.44% 67.19% 31.25%

Face Recognition Results on ORL Database

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NAME EERVERIFICATION RATE AT

1% FAR 0.1% FAR 0.01 % FAR

CWT_ONES 4.64% 87.14% 69.64% 47.80%

DB2_ONES 5.00% 87.14% 67.50% 44.29%

COIF2_ONES 4.64% 87.50 70.71% 47.14%

PCA 5.70% 83.44% 67.19% 31.25%

Conclusion:

• CWT, DWT Partitioning of face images performed. Novel OneS representation is formed using the sub bands.

• Further PCA analysis is performed on OneS representation and results are compared.

• DET Curves are used to compare the performance of OneS representation.

• Relative Face recognition improvement is 3.7% with OneS representation compared to PCA.

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References:

1. Cook, Jamie, Vinod Chandran, and Sridha Sridharan. "Multiscale representation for 3-D face recognition." Information Forensics and Security, IEEE Transactions on 2.3 (2007): 529-536.

2. W. zhao, R. Chellapa, P. J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM comput. Surveys, vol.35, no.4, pp. 399-458, 2003.

3. A. Martinez and A. Kak, “PCA versus LDA,” IEEE Trans. Pattern Anal. Mach. Intell., vol.23,no.2,pp.228-233, Feb 2001.

4. J. T. Chien and C. C. Wu, Discriminant Wavelet faces and nearest feature classifiers for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1644-1649, Dec. 2002.

5. L. L. Shen and L. Bai, “ A review on gabor wavelts for face recognition,” Pattern Anal. Appl., vol.9, pp. 273 - 292, 2006.

6. N. G. Kingsbury, “Shift invariance properties of the dual tree complex wavelet transform,” in Proc. ICASSP99, Phoenix, Az, Mar. 16-19, 1999.

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dt-cwt subband partitioning for fac recognition

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