Foveated Vision for Biologically Inspired Continuous Face Authentication

Abstract

In everyday life whenever people observe, interact or speak to each other, visual attention is mostly directed toward the other person’s face, particularly to the eyes and the nearby periocular regions. This is naturally reflected when the user interacts with their mobile phones in several usual activities, such as web access, payments and video calls. For this reason, the functionality of mobile devices is strongly affected by the design of the user interface. In this chapter, we propose a biologically inspired approach for continuous user authentication based on the analysis of the ocular regions. The proposed system is based on a modified version of the HMAX visual processing module. HMAX is a hierarchical model which has been conceived to mimic the basic neural architecture of the ventral stream of the visual cortex. The original HMAX model consists of four layers: S1, C1, S2 and C2. S1 and C1 represent the responses to a bank of orientation-selective Gabor filters. S2 and C2 represent the responses of simple and complex cells to other textural features. The discrimination power of HMAX in recognizing classes of objects is invariant to rotation and scale. The C1 layer, which is mainly responsible for the scale and rotation invariance, is implemented using a max-pooling operation, which may lose some spatial information. To overcome this problem while preserving the maximal visual acuity and hence the localization accuracy, we propose to augment the model by applying a retinal log-polar mapping. The log-polar mapping is an approximation of the retino-cortical mapping that is performed by the early stages of the primate visual system. Due to the high density of the cones in the fovea, the log-polar approximation of the space-variant distribution model of the photoreceptors can only be applied outside the foveal region. Therefore, the log-polar mapping is added to the HMAX model as a complementary stage to process the peripheral region of the grabbed images. In order to demonstrate the feasibility of the proposed approach to mobile scenarios, experimental results obtained from publicly available databases and image streams grabbed from mobile devices will be presented.