Online Regression of Grandmother-Cell Responses with Visual Experience Learning for Face Recognition

Grandmother cell is a term in neuroscience to imitate the simplistic notion that the brain has a separate neuron to represent every familiar face, with important properties of sparseness and invariance. This paper proposes a linear regression based classification model for face recognition, which learn a mapping from the training feature vectors to the grandmother-cell-like codes, with one unit corresponding to an individual. Two kinds of visual experiences are incorporated to enhance the generalization capability of the regression mapping. First, the regression model maps the intra-personal facial differences of the unknown faces to the zeros vectors, so that any similar variation on the familiar face would not affect the regression result. Second, to adapt to the evolution of facial appearance, the model feeds the selected testing images back to incrementally retrain the regression mapping, and decrement ally remove the influence of outdated training images, all in an unsupervised manner. Experiments results on Extended Yale B, FERET, and AR databases demonstrate the efficacy of the proposed regression based face recognition algorithms.