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.
A new wavelet-filtered-based Shifted- phase-encoded Joint Transform Correlation (WPJTC) technique has been
proposed for efficient face recognition. The proposed technique uses discrete wavelet decomposition for preprocessing
and can effectively accommodate various 3D facial distortions, effects of noise, and illumination variations. After
analyzing different forms of wavelet basis functions, an optimal method has been proposed by considering the
discrimination capability and processing speed as performance trade-offs. The proposed technique yields better
correlation discrimination compared to alternate pattern recognition techniques such as phase-shifted phase-encoded
fringe-adjusted joint transform correlator. The performance of the proposed WPJTC has been tested using the Yale facial
database and extended Yale facial database under different environments such as illumination variation, noise, and 3D
changes in facial expressions. Test results show that the proposed WPJTC yields better performance compared to
alternate JTC based face recognition techniques.
Existing face recognition systems are susceptible to spoofing attacks. So, Face liveness detection is a pivotal part for reliable face recognition, which has recently acknowledged vast attention. In this paper we propose a wavelet decomposition based face liveness recognition system using an energy calculation technique. Live faces contain high energy components compared to fake or printed image. In this paper, we calculate energy components of live face as well as fake face using discrete wavelet decomposition method. We analyze percentage of energy at different levels as well as for different wavelet basis function. We also analyze percentage of energy at different RGB bands and efficient face liveness detection method has been proposed. Discrete wavelet representation has been used to calculate decomposed energy components. Moreover, it provides differentiation of several spatial orientations as well as average and detailed information which are missing in the fake faces. This technique provides excellent discrimination capability when compared to the previously reported works based on the discrete Fourier transform and n-dimensional Fourier transform operations. To verify the proposed approach, we tested the performance using various face antispoofing datasets such as university of south Alabama (UFAD), and MSU face antispoofing dataset which incorporates different types of attacks. The test results obtained using the proposed technique shows better performance compared to existing techniques.
This paper presents an efficient phase-encoded and 4-phase shift keying (PSK)-based fringe-adjusted joint transform correlation (FJTC) technique for face recognition applications. The proposed technique uses phase encoding and a 4- channel phase shifting method on the reference image which can be pre-calculated without affecting the system processing speed. The 4-channel PSK step eliminates the unwanted zero-order term, autocorrelation among multiple similar input scene objects while yield enhanced cross-correlation output. For each channel, discrete wavelet decomposition preprocessing has been used to accommodate the impact of various 3D facial expressions, effects of noise, and illumination variations. The performance of the proposed technique has been tested using various image datasets such as Yale, and extended Yale B under different environments such as illumination variation and 3D changes in facial expressions. The test results show that the proposed technique yields significantly better performance when compared to existing JTC-based face recognition techniques.
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