Modeling Phase Spectra Using Gaussian Mixture Models for Human Face Identification
It has been established that information distinguishing one human face from another is contained to a large extent in the Fourier domain phase component of the facial image. However, to date, formal statistical models for this component have not been deployed in face recognition tasks. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) for the phase component for performing human identification. Classification and verification are performed using a MAP estimate and we show that we are able to achieve identification error rates as low as 2% and verification error rates as low as 0.3% on a database with 65 individuals with extreme illumination variations. The proposed method is easily able to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. A potential use of the method in illumination normalization is also discussed.
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