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.
Md. Moniruzzaman and Mohammad S. Alam, "2D DOST based local phase pattern for face recognition," Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030G (Presented at SPIE Defense + Security: April 13, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262806.
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