Recognition of facial expressions by cortical multi-scale line and edge coding

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.

Brightness perception: one simple model explaines most if not all effects

Few models can explain Mach bands (Pessoa, 1996 Vision Research 36 3205-3227) . Our own employs multiscale line and edge coding by simple and complex cells. Lines are interpreted by Gaussian functions, edges by bipolar, Gaussian-truncated errorfunctions. Widths of these functions are coupled to the scales of the underlying cells and the amplitudes are determined by their responses.

Looking through the eyes of the painter: from visual perception to non-photorealistic rendering

In this paper we present a brief overview of the processing in the primary visual cortex, the multi-scale line/edge and keypoint representations, and a model of brightness perception. This model, which is being extended from 1D to 2D, is based on a symbolic line and edge interpretation: lines are represented by scaled Gaussians and edges by scaled, Gaussian-windowed error functions. We show that this model, in combination with standard techniques from graphics, provides a very fertile basis for non-photorealistic image rendering.

Perception-based painterly rendering: funcionality and interface design

Painterly rendering (non-photorealistic rendering or NPR) aims at translating photographs into paintings with discrete brush strokes, simulating certain techniques (im- or expressionism) and media (oil or watercolour). Recently, our research into visual perception and models of processes in the visual cortex resulted in a new rendering scheme, in which detected lines and edges at different scales are translated into brush strokes of different sizes. In order to prepare a version which is suitable for many users, including children, the design of the interface in terms of window and menu system is very important. Discussions with artists and non-artists led to three design criteria: (1) the interface must reflect the procedures and possibilities that real painters follow and use, (2) it must be based on only one window, and (3) the menu system must be very simple, avoiding a jungle of menus and sub-menus. This paper explains the interface that has been developed.

Correlates of facial expressions in the primary visual cortex

Face detection and recognition should be complemented by recognition of facial expression, for example for social robots which must react to human emotions. Our framework is based on two multi-scale representations in cortical area V1: keypoints at eyes, nose and mouth are grouped for face detection [1]; lines and edges provide information for face recognition [2].

Image morphology: from perception to rendering

Complete image ontology can be obtained by formalising a top-down meta-language wich must address all possibilities, from global message and composition to objects and local surface properties.

The perception of woman on the example of the Odyssey of Homer and its english translation by James Huddleston and portuguese translation by Frederico Lourenço

Dissertação de mestrado, Estudos Literários e Artísticos, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2015