Robust face recognition by multiscale kernel associative memory models based on hierarchical spatial-domain Gabor transforms

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 quasi-complete Gabor transform, which are then modelled by kernel associative memories, The pyramidal multi-scale 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 occlusions and expression changes