We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research. Categories and Subject Descriptors: I.3.3 [Computer Graphics]: Picture/image Generation—bitmap and framebuffer operations;

Principal component analysis (PCA) of face images is here related to subjects'performance on the same images. In two experiments subjects were shown a set of faces and asked to rate them for distinctiveness. They were subsequently shown a superset of faces and asked to identify those which appeared originally. Replicating previous work, we found that hits and false positives (FPs) did not correlate: those faces easy to identify as being "seen" were unrelated to those faces easy to reject as being "unseen". PCA was performed on three data sets: (i) face images with eye-position standardised; (ii) face images morphed to a standard template to remove shape information; (iii) the shape information from faces only. Analyses based upon PCA of shape-free faces gave high predictions of FPs, while shape information itself contributed only to hits. Furthermore, while FPs were generally predictable from components early in the PCA, hits appear to be accounted for by later components. We conclude that shape and "texture" (the image-based information remaining after morphing) may be used separately by the human face processing system, and that PCA of images offers a useful tool for understanding this system.

ly, we applied principal component analysis to the pixel-coded face images with the aim of extracting measures related to the gender classifiability and recognizability of individual faces. We incorporated these model-derived measures into the factor analysis with the human rating and performance measures. This combined analysis indicated that face recognizability is related to the distinctiveness of a face with respect to its gender subcategory prototype. Additionally, the gender classifiability of faces related to at least one caricatured aspect of face gender. 1. introduction 1 Human faces provide us with a plethora of information that is valuable and necessary for social interaction. When we encounter a face, we can quickly and efficiently decide whether it is one we know. For faces of persons we know, we can often retrieve semantic and identity information about the person. Additionally, from both familiar and unfamiliar faces we can make judgments

We used functional magnetic resonance imaging to study the response properties of the human fusiform face area (FFA: Kanwisher, McDermott, & Chun, 1997) to a variety of face-like stimuli in order to clarify the functional role of this region. FFA responses were found to be (1) equally strong for cat, cartoon and human faces despite very different image properties, (2) equally strong for entire human faces and faces with eyes occluded but weaker for eyes shown alone, (3) equal for front and profile views of human heads, but declining in strength as faces rotated away from view, and (4) weakest for nonface objects and houses. These results indicate that generalisation of the FFA response across very different face types cannot be explained in terms of a specific response to a salient facial feature such as the eyes or a more general response to heads. Instead, the FFA appears to be optimally tuned to the broad category of faces.

east begin to see where some of these potential lines of inquiry may lie. The psychological concepts that we believe readers will find familiar in this chapter concern issues of measurement, representation, and task demands. These are issues encountered in nearly all models of psychological phenomena. Specifically, we will ask the following kinds of questions. How do you measure the information in a stimulus when the stimulus is a face? How do we represent sub-categories of stimuli, e.g., for faces, male and Quantitative Models of Face Cognition 3 female, young and old? Finally, how do the demands of the task and the nature of the processor constrain our access to and use of the information in the representation? This chapter is organized as follows. We first give a brief overview of the kinds of tasks we must accomplish with human faces. This defines the nature and diversity of the output that computational models must produce to be considered successful. We next pre

Several recent demonstrations using visual adaptation have revealed high-level aftereffects for complex patterns including faces. While traditional aftereffects involve perceptual distortion of simple attributes such as orientation or colour that are processed early in the visual cortical hierarchy, face adaptation affects perceived identity and expression, which are thought to be products of higher-order processing. And, unlike most simple aftereffects, those involving faces are robust to changes in scale, position and orientation between the adapting and test stimuli. These differences raise the question of how closely related face aftereffects are to traditional ones. Little is known about the build-up and decay of the face aftereffect, and the similarity of these dynamic processes to traditional aftereffects might provide insight into this relationship. We examined the effect of varying the duration of both the adapting and test stimuli on the magnitude of perceived distortions in face identity. We found that, just as with traditional aftereffects, the identity aftereffect grew logarithmically stronger as a function of adaptation time and exponentially weaker as a function of test duration. Even the subtle aspects of these dynamics, such as the power-law relationship between the adapting and test durations, closely resembled that of other aftereffects. These results were obtained with two different sets of face stimuli that differed greatly in their low-level properties. We postulate that the mechanisms governing these shared dynamics may be dissociable from the responses of feature-selective neurons in the early visual cortex.

This paper proposes the Face Multimedia Object (FMO), and iFACE as a framework for implementing the face object within multimedia systems. FMO encapsulates all the functionality and data required for face animation. iFACE implements FMO and provides necessary interfaces for a variety of applications in order to access FMO services. 1.

We have created a gradient-based face navigation interface that allows users to explore a large face space based on an eigenface technique. This approach to synthesizing faces contrasts with more typical techniques for forming composite faces based on the blending of facial features. We compare three ways of moving through the face space, using two types of sliders and a face-wheel. These are adapted from typical color space interfaces since they are commonly used. However, eigenface dimensions do not have meaningful text labels, unlike primary colors, necessitating the use of faces themselves for the labels of the navigation axes. Results suggest that users can navigate with face-labelled axes. They find slider interfaces best suited to finding the neighborhood of a target face, but that the face-wheel is better for refinement once inside the neighborhood.

Perception of facial expressions of emotion is generally assumed to correspond to underlying muscle movement. However, it is often observed that some individuals have sadder or angrier faces, even for neutral, motionless faces. Here, we report on one such effect caused by simple static configural changes. In particular, we show four variations in the relative vertical position of the nose, mouth, eyes, and eyebrows that affect the perception of emotion in neutral faces. The first two configurations make the vertical distance between the eyes and mouth shorter than average, resulting in the perception of an angrier face. The other two configurations make this distance larger than average, resulting in the perception of sadness. These perceptions increase with the amount of configural change, suggesting a representation based on variations from a norm (prototypical) face.

The faces of both adults and children can be classified accurately by sex, even in the absence of sex-stereotyped social cues such as hair and clothing (Wild et al., 2000). Although much is known from psychological and computational studies about the information that supports sex classification for adults' faces, children's faces have been much less studied. The purpose of the present study was to quantify and compare the information available in adults' versus children's faces for sex classification and to test alternative theories of how human observers distinguish male and female faces for these different age groups. We implemented four computational/neural network models of this task that differed in terms of the age categories from which the sex classification features were derived. Two of the four strategies replicated the advantage for classifying adults' faces found in previous work. To determine which of these strategies was a better model of human performance, we compared the performance of the two models with that of human subjects at the level of individual faces. The results suggest that humans judge the sex of adults' and children's faces using feature sets derived from the appropriate face age category, rather than applying features derived from another age category or from a combination of age categories. 2001 Cognitive Science Society, Inc. All rights reserved.