The Caucasian and North African French Faces (CaNAFF): A Face Database

Diverse Face Images (DFI): Validated for racial representation and eye gaze

Face processing is a central component of human communication and social engagement. The present investigation introduces a set of racially and ethnically inclusive faces created for researchers interested in perceptual and socio-cognitive processes linked to human faces. The Diverse Face Images (DFI) stimulus set includes high-quality still images of female faces that are racially and ethnically representative, include multiple images of direct and indirect gaze for each model and control for low-level perceptual variance between images. The DFI stimuli will support researchers interested in studying face processing throughout the lifespan as well as other questions that require a diversity of faces or gazes. This report includes a detailed description of stimuli development and norming data for each model. Adults completed a questionnaire rating each image in the DFI stimuli set on three major qualities relevant to face processing: (1) strength of race/ethnicity group associations, (2) strength of eye gaze orientation, and (3) strength of emotion expression. These validation data highlight the presence of rater variability within and between individual model images as well as within and between race and ethnicity groups.

Facial basis of stereotypes: Judgements of warmth and competence based on cross-group typicality/distinctiveness of faces

Human migration is an increasingly common phenomenon and migrants are at risk of disadvantageous treatment. We reasoned that migrants may receive differential treatment by locals based on the closeness of their facial features to the host average. Residents of Türkiye, the country with the largest number of refugees currently, served as participants. Because many of these refugees are of Arabic origin, we created target facial stimuli varying along the axis connecting Turkish and Arabic morphological prototypes (excluding skin colour) computed using geometric morphometrics and available databases. Participants made judgements of two universal dimensions of social perception-warmth and competence-on these faces. We predicted that participants judging faces manipulated towards the Turkish average would provide higher warmth and competence ratings compared to judging the same faces manipulated towards the Arabic average. Bayesian statistical tools were employed to estimate parameter values in multilevel models with intercorrelated varying effects. The findings did not support the prediction and revealed raters (as well as target faces) to be an important source of variation in social judgements. In the absence of simple cues (e.g. skin colour, group labels), the effect of facial morphology on social judgements may be much more complex than previously assumed.

GAN Inversion: A Survey

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model so that the image can be faithfully reconstructed from the inverted code by the generator. As an emerging technique to bridge the real and fake image domains, GAN inversion plays an essential role in enabling pretrained GAN models, such as StyleGAN and BigGAN, for applications of real image editing. Moreover, GAN inversion interprets GAN's latent space and examines how realistic images can be generated. In this paper, we provide a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation. We further discuss the trends and challenges for future research. A curated list of GAN inversion methods, datasets, and other related information can be found at https://github.com/weihaox/awesome-gan-inversion.

Line Drawings for Face Portraits From Photos Using Global and Local Structure Based GANs

Despite significant effort and notable success of neural style transfer, it remains challenging for highly abstract styles, in particular line drawings. In this paper, we propose APDrawingGAN++, a generative adversarial network (GAN) for transforming face photos to artistic portrait drawings (APDrawings), which addresses substantial challenges including highly abstract style, different drawing techniques for different facial features, and high perceptual sensitivity to artifacts. To address these, we propose a composite GAN architecture that consists of local networks (to learn effective representations for specific facial features) and a global network (to capture the overall content). We provide a theoretical explanation for the necessity of this composite GAN structure by proving that any GAN with a single generator cannot generate artistic styles like APDrawings. We further introduce a classification-and-synthesis approach for lips and hair where different drawing styles are used by artists, which applies suitable styles for a given input. To capture the highly abstract art form inherent in APDrawings, we address two challenging operations-(1) coping with lines with small misalignments while penalizing large discrepancy and (2) generating more continuous lines-by introducing two novel loss terms: one is a novel distance transform loss with nonlinear mapping and the other is a novel line continuity loss, both of which improve the line quality. We also develop dedicated data augmentation and pre-training to further improve results. Extensive experiments, including a user study, show that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.