Uncorrelated multilinear geometry preserving projections for multimodal biometrics recognition
We propose in this paper a novel supervised manifold learning algorithm, called uncorrelated multilinear geometry preserving projections (UMGPP), incorporating both the Fisher criterion and manifold criterion to learn multiple interrelated subspaces in an iterative manner for efficient multimodal biometric recognition. In contrast to the existing GPP algorithm, UMGPP learns multiple feature subspaces directly in higher order tensor space to preserve the structural information of original biometrics datum and obtains an increased number of uncorrelated projection directions, which enable UMGPP to out-perform GPP for multimodal biometrics recognition. Compared with other conventional information fusion-based multimodal recognition methods, UMGPP well exploits the relationship of different modality of the same individual and learns more efficient subspaces for feature extraction. Experimental results are presented to demonstrate the efficacy of the proposed method.