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In this paper, the authors have presented a new face recognition algorithm based on Region Covariance Matrix (RCM) descriptor computed in monogenic scale space. In the proposed model, energy information obtained using monogenic filter is used to represent a pixel at different scales to form region covariance matrix descriptor for each face image during training phase. An eigenvalue based distance measure is used to compute the similarity between face images. Extensive experimentation on AT&T and YALE face database has been conducted to reveal the performance of the monogenic scale space based region covariance matrix method and comparative analysis is made with the basic RCM method and Gabor based region covariance matrix method to exhibit the superiority of the proposed technique.
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