Face-Space Architectures: Evidence for the Use of Independent Color-Based Features (original) (raw)

Face processing: Human perception and principal components analysis

Memory & Cognition, 1996

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.

A multidimensional scaling analysis of own-and cross-race face spaces

Cognition, 2010

We examined predictions derived from Valentine's (1991) Multidimensional Space (MDS) framework for own-and other-race face processing. A set of 20 computerized faces was generated from a single prototype. Each face was saved as Black and White, changing only skin tone, such that structurally identical faces were represented in both race categories. Participants made speeded ''same-different" judgments to all possible combinations of faces, from which we generated psychological spaces, with ''different" RTs as the measure of similarity. Consistent with the MDS framework, all faces were pseudo-normally distributed around the (unseen) prototype. The distribution of faces was consistent with Valentine's (1991) predictions: despite their physical identity to the White faces, Black faces had lower mean inter-object distances in psychological space. Other-race faces are more densely clustered in psychological space, which could underlie well-known recognition deficits.

Structural aspects of face recognition and the other-race effect

Memory & Cognition, 1994

The other-race effect was examined in a series of experiments and simulations that looked at the relationships among observer ratings of typicality, familiarity, attractiveness, memorability, and the performance variables ofd’ and criterion. Experiment 1 replicated the other-race effect with our Caucasian and Japanese stimuli for both Caucasian and Asian observers. In Experiment 2, we collected ratings from Caucasian observers on the faces used in the recognition task. A Varimax-rotated principal components analysis on the rating and performance data for the Caucasian faces replicated Vokey and Read’s (1992) finding that typicality is composed of two orthogonal components, dissociable via their independent relationships to: (1) attractiveness and familiarity ratings and (2) memorahility ratings. For Japanese faces, however, we fond that typicality was related only to memorahility. Where performance measures were concerned, two additional principal components dominated by criterion and byd’ emerged for Caucasian faces. For the Japanese faces, however, the performance measures ofd’ and criterion merged into a single component that represented a second component of typicality, one orthogonal to thememorability-dominated component. A measure offace representation quality extracted from an autoassociative neural network trained with a majority of Caucasian faces and a minority of Japanese faces was incorporated into the principal components analysis. For both Caucasian and Japanese faces, the neural network measure related both to memorability ratings and to human accuracy measures. Combined, the human data and simulation results indicate that the memorahility component of typicality may be related to small, local, distinctive features, whereas the attractiveness/familiarity component may be more related to the global, shape-based properties of the face.

The face-space duality hypothesis: a computational model

Valentine's face-space suggests that faces are represented in a psychological multidimensional space according to their perceived properties. However, the proposed framework was initially designed as an account of invariant facial features only, and explanations for dynamic features representation were neglected. In this paper we propose, develop and evaluate a computational model for a twofold structure of the face-space, able to unify both identity and expression representations in a single implemented model. To capture both invariant and dynamic facial features we introduce the face-space duality hypothesis and subsequently validate it through a mathematical presentation using a general approach to dimensionality reduction. Two experiments with real facial images show that the proposed face-space: (1) supports both identity and expression recognition, and (2) has a twofold structure anticipated by our formal argument.

Contribution of Color to Face Recognition

Perception, 2002

One of the key challenges in face perception lies in determining how different facial attributes contribute to judgments of identity. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with gray-scale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

The structure of face-space is tolerant to lighting and viewpoint transformations

Journal of Vision, 2011

According to the face-space framework, faces are represented as locations in a multidimensional space, where the distance separating representations is proportional to the degree of dissimilarity between faces. The present study tested whether similarities between faces, and thus the structure of face-space, were tolerant to ("invariant" under) identitypreserving transformations such as changes in lighting or view. To examine the correspondence between the configurations of face-space under different transformations, perceived similarity was rated for two variants of a set of faces, differing either in illumination (Experiment 1) or viewpoint (Experiment 2). We found that similarity ratings within the first variant were highly correlated with ratings within the second variant. In addition, based on these ratings, a separate face-space was constructed for each variant using multidimensional scaling. Procrustean analysis revealed that the different spaces shared comparable structures. This correspondence serves as a face-space manifestation of the tolerance of identity representations. Accordingly, we suggest that tolerance may rely on the fact that similarities between faces under one transformation are isomorphic to similarity patterns under a different transformation. Thus, recognizing faces under varying viewing conditions may only require similarity evaluations withinVrather than acrossVdifferent transformations.

A comparison of two computer-based face recognition systems with human perceptions of faces

1998

The performance of two different computer systems for representing faces was compared with human ratings of similarity and distinctiveness, and human memory performance, on a specific set of face images. The systems compared were a graph-matching system (Lades M, Vorbrü ggen JC, Buhmann J, Lage J, von der Malsburg C, Wü rtz RP, Konen W. IEEE., Trans Comput 1993;42:300-311.) and coding based on principal components analysis (PCA) of image pixels (Turk M, Pentland A. J Cognitive Neurosci 1991;3:71-86.). Replicating other work, the PCA-based system produced very much better performance at recognising faces, and higher correlations with human performance with the same images, when the images were initially standardised using a morphing procedure and separate analysis of 'shape' and 'shape-free' components then combined. Both the graph-matching and (shape+ shape −free) PCA systems were equally able to recognise faces shown with changed expressions, both provided reasonable correlations with human ratings and memory data, and there were also correlations between the facial similarities recorded by each of the computer models. However, comparisons with human similarity ratings of faces with and without the hair visible, and prediction of memory performance with and without alteration in face expressions, suggested that the graph-matching system was better at capturing aspects of the appearance of the face, while the PCA-based system seemed better at capturing aspects of the appearance of specific images of faces.