Human Perception of Visual Realism for Photo and Computer-Generated Face Images

Abstract

Computer-generated (CG) face images are common in video games, advertisements, and other media. CG faces vary in their degree of realism, a factor that impacts viewer reactions. Therefore, efficient control of visual realism of face images is important. Efficient control is enabled by a deep understanding of visual realism perception: the extent to which viewers judge an image as a real photograph rather than a CG image. Across two experiments, we explored the processes involved in visual realism perception of face images. In Experiment 1, participants made visual realism judgments on original face images, inverted face images, and images of faces that had the top and bottom halves misaligned. In Experiment 2, participants made visual realism judgments on original face images, scrambled faces, and images that showed different parts of faces. Our findings indicate that both holistic and piecemeal processing are involved in visual realism perception of faces, with holistic processing becoming more dominant when resolution is lower. Our results also suggest that shading information is more important than color for holistic processing, and that inversion makes visual realism judgments harder for realistic images but not for unrealistic images. Furthermore, we found that eyes are the most influential face part for visual realism, and face context is critical for evaluating realism of face parts. To the best of our knowledge, this work is a first realism-centric study attempting to bridge the human perception of visual realism on face images with general face perception tasks.

Those Virtual People all Look the Same to me: Computer-Rendered Faces Elicit a Higher False Alarm Rate Than Real Human Faces in a Recognition Memory Task

“…Given that any experimental comparison between unmatched real and virtual faces would be confounded by differences in spatial frequency contents (e.g., overall lack of details in virtual faces), we hence decided to adopt the strict matching procedure for our second experiment. Our other findings replicate the previous finding (Fan et al, 2012 , 2014 ; Farid and Bravo, 2012 ), with slightly better controlled stimuli, that real and virtual faces are differentiated better and with higher confidence from color as compared with grayscale images. A closer inspection of false alarm rates as well as participants' confidence ratings suggested that colors are particularly important for the correct recognition of virtual faces.…”

“…Hence, our secondary research question is whether colors truly contribute to differentiating virtual from real faces. Previous studies suggest that real and virtual faces are easier to discriminate from color than grayscale images (Fan et al, 2012 , 2014 ; Farid and Bravo, 2012 ). However, given that these studies used different image sets for real and virtual faces, it is conceivable that these results would reflect differences between the employed image samples.…”

MesoNet: a Compact Facial Video Forgery Detection Network

“…Indeed, microscopic analyses based on image noise cannot be applied in a compressed video context where the image noise is strongly degraded. Similarly, at a higher semantic level, human eye struggles to distinguish forged images [21], especially when the image depicts a human face [1,7]. That is why we propose to adopt an intermediate approach using a deep neural network with a small number of layers.…”

Virtual Faces Evoke Only a Weak Uncanny Valley Effect: An Empirical Investigation With Controlled Virtual Face Images

“…Inversion is considered a hallmark of perceptual expertise, and it Running head: Weak Uncanny Valley for Controlled Face Images typically manifests itself as the much slower and less accurate processing of inverted (rotated 180 degrees) as compared with upright faces (Maurer, Le Grand, & Mondloch, 2002). Previous evidence suggests that inversion also affects human-likeness judgments such that it makes human faces more difficult to recognize as human but it does not affect the recognition of artificial faces (Fan et al, 2014). More specifically, inversion elicits decreased human-likeness and increased eeriness (Almaraz, 2017) as well as less frequent attributions of mind (Deska, Almaraz, & Hugenberg, 2017) for faces residing on the right side of the category boundary that separates human from artificial faces.…”