The 4D Space-Time Dimensions of Facial Perception

Facial cosmetic injection: A bibliometric analysis of research status and hotspots

BACKGROUND

The increasing popularity of cosmetic injections using various fillers and neuromodulators for facial rejuvenation has brought both new opportunities and challenges to this field.

AIM

Our study was designed to employ bibliometric and visual analysis for a qualitative and quantitative evaluation of facial cosmetic injections, as well as to identify research trends and hotspots in this field.

METHODS

All publications covering facial cosmetic injection during 2002-2023 were retrieved and extracted from the Web of Science database. The VOSviewer 1.6.18 software and the online tool (http://bibliometric.com/) were applied to analyze the publication trend.

RESULTS

A total of 3797 articles related to facial cosmetic injection were identified during the period 2002-2023. The United States had the largest volume of publications (1520, 40.0%), followed by China (333, 8.8%) and Germany (282, 7.3%). Among the institutions and journals, the University of California system and Plastic and Reconstructive Surgery accounted for the most papers related to facial cosmetic injection, respectively. Facial anatomy and injection techniques, prevention and management of complications, regenerative medicine, efficacy and safety of various soft-tissue fillers, as well as botulinum toxin injections for facial rejuvenation were identified as hotspots for facial cosmetic injections.

CONCLUSIONS

Facial cosmetic injections are showing an increasing trend in terms of both the number of published papers and operations performed. Despite the notable advancements in this field, numerous challenges persist, including safety concerns and the level of research evidence. With the emergence of novel technologies and materials, scholars from diverse countries and institutions should engage in more extensive collaboration, thereby directly expediting the progress of this field.

Face Recognition Characteristics in Patients with Age-Related Macular Degeneration Determined Using a Virtual Reality Headset with Eye Tracking

BACKGROUND AND OBJECTIVES

Face recognition is one of the most serious disabilities of patients with age-related macular degeneration (AMD). Our purpose was to study face recognition using a novel method incorporating virtual reality (VR) and eye tracking.

MATERIALS AND METHODS

Eighteen patients with AMD (seven male; median age 83 years; 89% with bilateral advanced AMD) and nineteen healthy controls (five male; median age 68 years) underwent the face recognition test IC FACES (Synthesius, Ljubljna, Slovenia) on a VR headset with built-in eye tracking sensors. Analysis included recognition accuracy, recognition time and fixation patterns. Additionally, a screening test for dementia and imaging with fundus autofluorescence and optical coherence tomography was performed.

RESULTS

AMD patients had significantly lower face recognition accuracy (42% vs. 92%; p < 0.001) and longer recognition time (median 4.0 vs. 2.0 s; p < 0.001) in comparison to controls. Both parameters were significantly worse in patients with lower visual acuity. In both groups, eye-tracking data revealed the two classical characteristics of the face recognition process, i.e., fixations clustering mainly in the nose-eyes-mouth triangle and starting observation in the nasal area.

CONCLUSIONS

The study demonstrates usability of a VR headset with eye tracking for studying visual perception in real-world situations which could be applicable in the design of clinical studies.

Development of the RIKEN database for dynamic facial expressions with multiple angles

The development of facial expressions with sensing information is progressing in multidisciplinary fields, such as psychology, affective computing, and cognitive science. Previous facial datasets have not simultaneously dealt with multiple theoretical views of emotion, individualized context, or multi-angle/depth information. We developed a new facial database (RIKEN facial expression database) that includes multiple theoretical views of emotions and expressers' individualized events with multi-angle and depth information. The RIKEN facial expression database contains recordings of 48 Japanese participants captured using ten Kinect cameras at 25 events. This study identified several valence-related facial patterns and found them consistent with previous research investigating the coherence between facial movements and internal states. This database represents an advancement in developing a new sensing system, conducting psychological experiments, and understanding the complexity of emotional events.

Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases

In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.