Power-law radon-transformed superimposed inverse filter synthetic discriminant correlator for facial recognition

Bahareh Haji-saeed; Jed Khoury; Charles L. Woods; John Kierstead

doi:10.1117/12.777627

17 March 2008 Power-law radon-transformed superimposed inverse filter synthetic discriminant correlator for facial recognition

Bahareh Haji-saeed, Jed Khoury, Charles L. Woods, John Kierstead

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Proceedings Volume 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008; 69730M (2008) https://doi.org/10.1117/12.777627
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

A power-law correlation based on an inverse filter Fourier-Radon-transform synthetic discriminant function (SDF) for facial recognition is proposed. In order to avoid spectral overlap and nonlinear crosstalk, superposition of rotationally variant sets of inverse filter Fourier-transformed Radon-processed templates is used to generate the SDF. For the inverse filter, the Fourier transform of M projections (Radon Transform) from one training image is combined with (N-1) M Fourier transform of M projections taken from another N-1 training image. This synthetic SDF filter has a very high discrimination capability; however, it is not noise robust. To overcome this problem, a power-law dynamic range compression is added to the correlation process. The proposed filter has three advantages: (1) high discrimination capability as an inverse filter, (2) noise robustness due to dynamic range compression, and (3) crosstalk-free nonlinear processing. The filter performance was evaluated by established metrics, such as peak-to-correlation energy (PCE), Horner efficiency, and correlation-peak intensity. The results showed significant improvement as the power-law filter compression increased.

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Bahareh Haji-saeed, Jed Khoury, Charles L. Woods, and John Kierstead "Power-law radon-transformed superimposed inverse filter synthetic discriminant correlator for facial recognition", Proc. SPIE 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008, 69730M (17 March 2008); https://doi.org/10.1117/12.777627

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KEYWORDS

Electronic filtering

Signal to noise ratio

Image filtering

Facial recognition systems

Nonlinear filtering

Fourier transforms

Interference (communication)

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