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.
Md. Moniruzzaman and Mohammad S. Alam, "Wavelet filtered shifted phase-encoded joint transform correlation for face recognition," Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 1020308 (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262562.
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