Robust Face Recognition by Multiscale Kernel Associative Memory Models Based on Hierarchical Spatial-Domain Gabor Transforms

摘要

Face recognition can be considered as a one-class classification problem and associative memory (AM) based approaches have been proven efficient in previous studies. In this paper, a Kernel Associative Memory (KAM) based face recognition scheme with a Multiscale Gabor transform, is proposed. In our method, face images of each person are first decomposed into their multiscale representations by a quasicomplete Gabor transform, which are then modelled by erne el Associative Memories. The pyramidal multiscale Gabor wavelet transform not only provides a very efficient implementation of Gabor transform in spatial domain, but also permits a fast reconstruction. In the testing phase, a query face image is also represented by a Gabor multiresolution pyramid and the recalled results from different KAM models corresponding to even Gabor channels are then simply added together to provide a reconstruction. The recognition scheme was thoroughly tested using several benchmark face datasets, including the AR faces, UMIST faces, JAFFE faces and Yale A faces. The experiment results have demonstrated strong robustness in recognizing faces under different conditions, particularly the poses alterations, varying occulusions and expression changes.