2D DOST based local phase pattern for face recognition

Md. Moniruzzaman; Mohammad S. Alam

doi:10.1117/12.2262806

1 May 2017 2D DOST based local phase pattern for face recognition

Md. Moniruzzaman, Mohammad S. Alam

Proceedings Volume 10203, Pattern Recognition and Tracking XXVIII; 102030G (2017) https://doi.org/10.1117/12.2262806
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

A new two dimensional (2-D) Discrete Orthogonal Stcokwell Transform (DOST) based Local Phase Pattern (LPP) technique has been proposed for efficient face recognition. The proposed technique uses 2-D DOST as preliminary preprocessing and local phase pattern to form robust feature signature which can effectively accommodate various 3D facial distortions and illumination variations. The S-transform, is an extension of the ideas of the continuous wavelet transform (CWT), is also known for its local spectral phase properties in time-frequency representation (TFR). It provides a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information while maintaining a direct relationship with the Fourier spectrum which is unique in TFR. After utilizing 2-D Stransform as the preprocessing and build local phase pattern from extracted phase information yield fast and efficient technique for face recognition. The proposed technique shows better correlation discrimination compared to alternate pattern recognition techniques such as wavelet or Gabor based face recognition. The performance of the proposed method has been tested using the Yale and extended Yale facial database under different environments such as illumination variation and 3D changes in facial expressions. Test results show that the proposed technique yields better performance compared to alternate time-frequency representation (TFR) based face recognition techniques.

Conference Presentation

Citation Download Citation

Md. Moniruzzaman and Mohammad S. Alam "2D DOST based local phase pattern for face recognition", Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030G (1 May 2017); https://doi.org/10.1117/12.2262806

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