Face recognition by cortical multi-scale line and edge representations

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

Face normalization using multi-scale cortical keypoints

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 extractions. Simple, complex and end-stopped cells tuned to different spatial frequencies (scales) and/or orientations provide input for line, edge and keypoint detection. This yields a rich, multi-scale object representation that can be stored in memory in order to identify objects. The multi-scale, keypoint-based saliency maps for Focus-of-Attention can be explored to obtain face detection and normalization, after which face recognition can be achieved using the line/edge representation. In this paper, we focus only on face normalization, showing that multi-scale keypoints can be used to construct canonical representations of faces in memory

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 neurobiological basis of inter-individual variability in visual perception

Visual perception is the conscious experience that is unique to each individual. However, conventional neuroscience studies tend to focus on the commonality in visual perception across different individuals and fail to address the key properties of any conscious experience - its individuality and subjectivity. In my thesis, I investigated the neurobiological basis of perceptual variability across healthy human adults, through a combined approach of psychophysics, in-vivo MR imaging, in-vitro histological imaging, and computational modeling. I found that perception of local and global visual features, as assessed respectively from visual discrimination of local feature details and visual illusion induced by global feature contexts, exhibits a ten-fold inter-individual variability that correlates with the morphology of primary visual cortex. Specifically, an increase in the surface area of primary visual cortex is associated with a shift in the scope of visual feature perception from global-context-oriented to local-detail-oriented, where individuals with smaller visual cortical surface area experience stronger visual illusion and individuals with larger visual cortical surface area perform more accurate visual discrimination. Intriguingly, an increase in the thickness of primary visual cortex has the opposite impact, where visual discrimination is less accurate at visual field locations corresponding to thicker parts of primary visual cortex. The functional impact that visual cortical anatomy exerts on visual feature perception is recapitulated in visual neural selectivity. I found that in individuals with larger surface area of primary visual cortex, visual cortical neurons exhibit higher selectivity and respond to a smaller, localised visual field range. By contrast, at thicker parts of primary visual cortex, visual cortical neurons exhibit lower selectivity and respond to a larger, globalised visual field range. The opposite functional impacts exhibited by the two morphological dimensions, the surface area and the thickness, of primary visual cortex can nonetheless be unified under the framework of intracortical scaling. I found that the scaling of intracortical connectivity with visual cortical morphology shifts the scope of both visual feature perception and visual neural selectivity between global- and local-oriented. Together these findings revealed that the individuality in visual feature perception arises neurobiologically from the variability in visual cortical morphology, through the mediation of intracortical connectivity and visual neural selectivity