Predominance of eyes and surface information for face race categorization

Ethnic and Sex Diversity in Academic Plastic Surgery: A Cross-sectional Study

Ethnic, racial, and sex disparities continue to persist in medicine despite efforts to diversify the profession. In competitive surgical specialties such as plastic surgery, those disparities are particularly pronounced. This study aims to evaluate racial, ethnic, and sex diversity in academic plastic surgery.

Methods

We compiled a list of major plastic surgery professional societies, plastic surgery journal editorial boards, and plastic surgery accreditation boards to evaluate ethnic and sex diversity in society, research, and accreditation domains, respectively. Demographic data were collected and analyzed using the Mann-Whitney U test and the Kruskal-Wallis test.

Results

White individuals are significantly overrepresented across the professional and research domains, and Asian individuals are overrepresented in the professional domain when compared to non-white races. White individuals make up a total of 74% of the society domain, 67% of the research domain, and 86% of the accreditation domain when compared to all non-white surgeons. Male surgeons made up 79% of the society domain, 83% of the research domain, and 77% of the accreditation domain when compared to all non-male surgeons.

Conclusions

Ethnic, racial, and sex disparities persist in academic plastic surgery. This study, which looked at societies, editorial boards, and accreditation boards, demonstrated a persistent ethnic, racial, and sex homogeneity among leadership. Changes are required to continue to diversify the field and provide women and underrepresented minorities the tools needed to succeed.

Corneal reflections and skin contrast yield better memory of human and virtual faces

Virtual faces have been found to be rated less human-like and remembered worse than photographic images of humans. What it is in virtual faces that yields reduced memory has so far remained unclear. The current study investigated face memory in the context of virtual agent faces and human faces, real and manipulated, considering two factors of predicted influence, i.e., corneal reflections and skin contrast. Corneal reflections referred to the bright points in each eye that occur when the ambient light reflects from the surface of the cornea. Skin contrast referred to the degree to which skin surface is rough versus smooth. We conducted two memory experiments, one with high-quality virtual agent faces (Experiment 1) and the other with the photographs of human faces that were manipulated (Experiment 2). Experiment 1 showed better memory for virtual faces with increased corneal reflections and skin contrast (rougher rather than smoother skin). Experiment 2 replicated these findings, showing that removing the corneal reflections and smoothening the skin reduced memory recognition of manipulated faces, with a stronger effect exerted by the eyes than the skin. This study highlights specific features of the eyes and skin that can help explain memory discrepancies between real and virtual faces and in turn elucidates the factors that play a role in the cognitive processing of faces.

Eyes versus Eyebrows: A Comprehensive Evaluation Using the Multiscale Analysis and Curvature-Based Combination Methods in Partial Face Recognition

This work aimed to find the most discriminative facial regions between the eyes and eyebrows for periocular biometric features in a partial face recognition system. We propose multiscale analysis methods combined with curvature-based methods. The goal of this combination was to capture the details of these features at finer scales and offer them in-depth characteristics using curvature. The eye and eyebrow images cropped from four face 2D image datasets were evaluated. The recognition performance was calculated using the nearest neighbor and support vector machine classifiers. Our proposed method successfully produced richer details in finer scales, yielding high recognition performance. The highest accuracy results were 76.04% and 98.61% for the limited dataset and 96.88% and 93.22% for the larger dataset for the eye and eyebrow images, respectively. Moreover, we compared the results between our proposed methods and other works, and we achieved similar high accuracy results using only eye and eyebrow images.

Eye movement strategies in face ethnicity categorization vs. face identification tasks

A quick look at a face allows us to identify the person, their gender, and emotion. Humans direct their first eye movement towards points on the face that vary moderately across these common tasks and maximize performance. However, not known is the extent to which humans alter their oculomotor strategies to maximize accuracy in more specialized face categorization tasks. We studied the eye movements of Indian observers during a North vs. South Indian face categorization task and compared them to those in a person-identification task. We found that observers did not alter their first eye movement strategy for the ethnic categorization task, i.e., they directed their first fixations to a similar preferred point as in the person-identification task. To assess whether using a similar preferred point of fixation for both tasks resulted in a performance cost for the categorization task, we measured performance as a function of fixation position along the face. Fixating away from the preferred point of fixation reduced observer performance in the person identification task, but not in the ethnicity categorization task. We used computational modeling to assess whether the results could be explained by an interaction between the distribution of task information across the face and the foveated properties of the visual system. A foveated ideal observer analysis revealed a spatially more distributed task information and lower dependence of performance on the point of fixation for the ethnicity categorization task relative to the person identification. We conclude that, unlike the person identification task, humans can access the information for the ethnicity categorization task from various points of fixation. Thus, the observer strategy to utilize the typical person identification first eye movement for the ethnicity categorization task is a simple solution that incurs little or no performance cost.