Color Face Research Articles

Taking the Glass Escalator Theory to School

White men have realized persistent advantages in high-status leadership positions in the workplace. Theories of gender inequality in leadership, including the “glass escalator” theory, consider only how this issue functions within occupational contexts and have yet to assess whether or how such patterns of inequality occur across institutions and may persist throughout the education-to-occupation pipeline. Using nationally representative data on U.S. high school students, the authors test for early evidence of racialized glass escalator effects by examining leadership disparities within the feminine-coded field of performing arts activities, which serve as an upstream approximation of feminized occupations. The authors find that although boys are less likely to participate in performing arts than girls, conditional on participating, they are significantly more likely to be leaders. White boys, in particular, have a strong leadership advantage compared with all other schoolmates, and especially to girls of color. The significant disadvantage girls of color face relative to White boys in attaining leadership within feminized domains in high school parallels established patterns in the occupational sphere. To the extent that early leadership experience begets future skills, opportunities, and pursuit of leadership positions, such early disadvantages may be expected to compound over the life course, influencing gendered and racialized role expectations and sorting.

Face spoofing detection using Heterogeneous Auto-Similarities of Characteristics

Facial recognition systems are widely used in various daily applications due to their high accuracy rate and ease of use. Unfortunately, their performance drops drastically when encountering spoofing attacks. In this paper, a novel pipeline for face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces is proposed. To detect print and replay spoofing attacks, the HASC descriptor encodes linear and non-linear dependencies between low-level dense features of the color face image using covariance and information-theoretic measures. The combination of covariance and information-theoretic measures makes HASC an efficient descriptor to explore how the features are interrelated in real and fake faces. The HASC texture descriptions are extracted separately from each color channel of the HSV and YCbCr color spaces, and the resulting feature vectors are concatenated into an extended feature vector to better discriminate between real and fake faces. Extensive experiments are conducted on publicly available databases namely, Replay-Attack, CASIA FASD, and MSU MFSD. The obtained results demonstrate that while being of low dimension, the HASC descriptor when fed into a binary Support Vector Machine (SVM) classifier, achieves competitive results compared to the state-of-the-art.

Reactivity of observers' facial skin blood flow depending on others' facial expressions and blushing.

Facial skin blood flow (SkBF) has attracted attention as an autonomic indicator because it influences facial colour, which informs others of emotional states, and facial temperature related to social anxiety. Previous studies have examined the facial SkBF in people experiencing emotions; however, facial SkBF changes in the observers of emotions are poorly understood. Our study clarified facial SkBF changes related to observing others' emotions by comparing the changes with other physiological indices. Thirty healthy participants (24 females; mean age: 22.17) observed six types of facial expressions (neutral, angry, and embarrassed expressions with and without facial blushing) and rated the emotional intensity of the other person. We measured their facial SkBF, finger SkBF, and cardiac RR interval as they made their observations. Facial SkBF generally decreased in relation to observing emotional faces (angry and embarrassed faces) and significantly decreased for angry expressions with blushing. None of the participants noticed blushing of facial stimuli. For the RR interval and finger SkBF, there was no variation depending on the observed facial expressions, although there was a general increase related to observation. These results indicated that facial SkBF is sensitive and reactive to emotional faces-especially angry faces with blushing- compared with other autonomic indices. The facial SkBF changes were not related to either RR interval changes or the intensity rating, suggesting that facial SkBF changes may be caused by vasoconstriction and have potential functions for our emotions. The decrease in facial SkBF may have a role in calming observers by preventing them from adopting the same emotional state as a person with intense anger. These findings clarify daily facial SkBF fluctuations and their relationship with our emotional processing in interpersonal situations.

Ecommerce and environmental justice in metro Seattle

Urban distribution centers (UDCs) are opening at unprecedented rates to meet rising home delivery demand. The trend has raised concerns over the equity and environmental justice implications of ecommerce's negative externalities. However, little research exists connecting UDC location to the concentration of urban freight-derived air pollution among marginalized populations. Using spatial data of Amazon UDCs in metropolitan Seattle, this study quantifies the socio-spatial distribution of home delivery-related commercial vehicle kilometers traveled (VKT), corresponding air pollution, and explanatory factors. Results reveal that racial and income factors are relevant to criteria air pollutant exposure caused by home deliveries, due to tracts with majority people of color being closer in proximity to UDCs and highways. Tracts with majority people of color face the highest median concentration of delivery vehicle activity and emissions despite ordering less packages than white populations. While both cargo van and heavy-duty truck emissions disproportionately affect people of color, the socio-spatial distribution of truck emissions shows higher sensitivity to fluctuations in utilization. Prioritizing environmental mitigation of freight activity further up the urban distribution chain in proximity to UDCs, therefore, would have an outsized impact in minimizing disparities in ecommerce's negative externalities.

Cold sensitivity classification using facial image based on convolutional neural network

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system.Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set.Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important.Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

Effects of facial skin pigmentation on social judgments in a Mexican population.

People quickly and involuntarily form impressions of others based on their facial physical attributes, which can modulate critical social interactions. Skin pigmentation is one of the most variable and conspicuous facial traits among human populations. Empirical evidence suggests that these variations reflect ancestral ecological selective pressures balancing cutaneous vitamin D synthesis with the protection of the dermis from ultraviolet radiation. Nevertheless, skin pigmentation may currently be subject to additional selective pressures. For instance, the colonial era in Central and South America developed a highly stratified society based on ethnic origins, and light skin pigmentation became associated with higher social status and deference. This association could have originated through historical social learning that promoted favorable social perceptions towards individuals with lighter skin color and unfavorable perceptions towards individuals with darker skin color, which could still be present in the perception of current populations. Facial skin pigmentation is also sexually dimorphic, with males tending to exhibit darker skin than females, a difference that could be driven by sexual selection. To explore whether social learning and sexual selection represent additional selective pressures on skin pigmentation, we tested how this facial trait influences fundamental social perceptions in a Mexican population (N = 700, 489 female). We sampled facial images of eight European American males with natural lighter facial skin and eight males from an indigenous pre-Columbian community from Mexico, the Me'Phaa, with natural darker facial skin. We produced stimuli from these images by varying the skin pigmentation while preserving the facial shape. Stimuli were rated on attractiveness, trustworthiness, perceived health, dominance, aggressiveness, and femininity/masculinity. We found that the natural light-skinned faces were perceived as more attractive, trustworthy, and healthy but less dominant than the natural dark faces. Furthermore, by varying the facial skin color in these original groups, we altered the perceptions of them, mainly their attractiveness. These results partially support the hypothesis that dark facial skin color may help males compete for mates. Also, the results strongly support the view that lighter facial skin color became associated with social benefits through social learning in this Mexican population. Our findings, when viewed through the lens of cultural evolution, align with previous research in social psychology and anthropology. They hold the potential to offer a comprehensive understanding of the origin of this social phenomenon of cultural transmission, which currently plays a role in the formation of racial attitudes, stereotyping, and racial inequality in Mexican and other Latin American populations.

Phenotypic characterization and genetic diversity of indigenous chickens of Jordan in comparison with native and commercial breeds ‎for conservation and breeding purposes

Indigenous chickens of Jordan are facing extinction and need genetic conservation because they were unable to commercially compete in poultry industry because of low genetic ability ‎compared to commercial layers. The study aimed to investigate phenotypic and genetic diversity ‎of village chickens in the Jordan using discriminant analyses procedures to provide a basis for ‎sustainable genetic conservation and utilization program to overcome any possible extinction. The ‎sampled chicken population of 578 one-year old chickens (125 males and 433 females) was ‎phenotypically characterized for 15 biometric and plumage traits from major cities of the three ‎regions; Middle, North, and South. The traits variations within and between breeds were detected ‎statistically by stepwise discriminant and canonical-discriminant of uni- and multivariate analyses. ‎The results showed the sampled population as village chickens in Jordan is comprised of ‎indigenous (Baladi) breed (85%) and few exotic and commercial breeds. The breeds were distinct ‎and differentiated based on phenotypic traits indicating high genetic variability. The major ‎phenotypic traits that showed significant power to differentiate breeds were comb type, body ‎weight, comb size, earlobe color, wattle size, face color and breast size in males and comb type and ‎size, body weight, face and breast size, leg color and wattle size in females. Recent and past ‎crossings, admixture or migration from exotic and commercial breeds were noted. Moreover, low ‎levels of phylogeographic structure were observed across the studied breeds. In conclusion, there is need to conserve the indigenous breed in situ and in vivo for its adaptive gene pool in coming ‎days of persisted climate change and diseases threats.

Improve Image White Balance by Facial Skin Color

Improve Image White Balance by Facial Skin Color

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

“We Know About Things Too”: Exploring the Labors of Love Involved in Cultivating Youth Voice in Online Youth Civic Engagement Programs With Youth of Color

Online youth civic engagement programs are often designed to support the cultivation of youth voice, yet working with youth of color who are particularly skeptical of civic life takes a certain form of labor that often remains unexamined in the scholarship of youth civic engagement. Drawing on concepts of invisible, emotional, and relational labor and the work of critical race theory (CRT), this article examines what is often termed the “labor of love” that characterizes the behind-the-scenes work. Utilizing a critical ethnographic approach, we identify three stages in the labor of love involved in cultivating youth voice in five different online youth civic engagement programs as we sought to highlight youth voice, perspective, and expertise with local policymakers. We argue that the behind-the-scenes invisible, emotional, and relational labor needs to be better understood to address the barriers youth of color face in relation to gaining full access to democratic participation.

The rural teacher shortage

There is much alarm about the current teacher shortage resulting from the pandemic and its aftermath. But teacher shortages have long been a perennial issue in K-12 education. Researchers Richard Ingersoll and Henry Tran analyzed data from the National Center of Education Statistics to compare rural schools to urban and suburban schools to understand their teacher staffing problems. They found that rural teacher shortages are worse than elsewhere and are driven by teacher turnover and hence won’t be solved just by focusing on new teacher recruitment. High-poverty rural schools and rural schools with high levels of students of color face the most extreme teacher turnover of all schools, experiencing departures of between a quarter and a third of teachers annually. Their reasons for leaving include lack of classroom autonomy and the inability to be included in schoolwide decision making.

Face Sketch to Image Generation using Generative Adversarial Network

Numerous studies have been conducted in the area of sketch to picture conversion and they got the good outcomes, but sometimes it is not accurate that they observed the blurry boundaries, the mixing of two colors that is the color of hair and face or mixing of both. These results are of the convolution neural networks that are basic of GAN. So to overcome their drawbacks we proposed a novel generative adversarial network using conditional GAN. For that we converted the original image in sketch and both the sketch and original image as reference is applied as input. We got more realistic and sharp colored images as compared to other. We focused on the feature detection, and the results are good. For the experimentation we used the STL-10 dataset. We overcome the problem of mixing of colors and got the different colors for hair, lips, and skin using conditional GAN as compared to CNN modern with increased performance and precision.

Algebraic algorithms for eigen-problems of a reduced biquaternion matrix and applications

In recent years, the reduced biquaternion algebras have been widely used in color image processing problems and in the field of electromagnetism. This paper studies eigen-problems of reduced biquaternion matrices by means of a complex representation of a reduced biquaternion matrix and derives new algebraic algorithms to find the eigenvalues and eigenvectors of reduced biquaternion matrices. This paper also concludes that the number of eigenvalues of an n×n reduced biquaternion matrix is infinite. In addition, the proposed algebraic algorithms are shown to be effective in application to a color face recognition problem.

Immunoactivation Affects Perceived Body Odor and Facial but Not Vocal Attractiveness.

Several previous studies have shown that in mammals, the health status of conspecifics can be assessed based on perceptual cues. Olfactory, visual, or acoustic cues may lead to avoidant behavior, thus reducing the risk of contagion by close contact with infected individuals. We tested whether immune system activation after immunization leads to perceptible changes in body odor and facial and vocal attractiveness in humans. We have experimentally activated the immune system of male participants using vaccination against hepatitis A/B and meningococcus. Their body odor, facial photographs, and vocal recordings were collected before and 14 days after vaccination. Subsequently, the body odor samples, facial photographs, and vocal recordings were assessed by female raters for their attractiveness and healthiness. We have also measured skin coloration (from facial photographs and in vivo using a spectrophotometer), vocal parameters, and C-reactive protein (CRP) levels as a marker of inflammation. We found an increase in perceived body odor attractiveness, a decrease in facial attractiveness and healthiness, and no change in vocal attractiveness 14 days after vaccination compared to the prevaccination condition. Moreover, there was no change in facial coloration or vocal parameters between the prevaccination and postvaccination conditions. Prevaccination CRP levels were negatively associated with body odor and facial attractiveness and positively associated with body odor intensity. Overall, our results suggest that perceived body odor as well as facial but not vocal attractiveness may provide cues to activation of the immune response and that each modality may carry specific information about the individual's condition.

Socio-economic Profile of Equine Owner, Morphometric Characterization and Reproductive Performance of Indigenous Horses in Bangladesh

Background: The socio-economic importance of horses plays a decisive role in the dynamism of rural economies in remote regions of developing countries such as Bangladesh. Horses are particularly suitable for transportation and recreation in these areas. Methods: The study was conducted from January to March 2023 in 200 randomly selected horse families concentrated in Sherpur, Jamalpur, Tangail, Sirajganj, Natore, and Noagoan districts of Bangladesh. After the necessary corrections and modifications, the final questionnaire was created based on the objectives of the study. The data were collected by respondents individually through personal interviews. Results: The average number of horses per family was 1.18, with 70% of them being transported and only 11.5% participating in horse races. The average peak and the poor seasonal income of the farmers were recorded as BDT (12,920-6,717). On average, 2 kg of concentrate feed per day is supplied to each horse with forages in the morning and afternoon depending on the situation. All farmers practiced natural mating, but they did not perform vaccination (100%). The indigenous horse's face color was observed as a star of 30%, and 45% non-specific, while the coat color was chestnut 38.5%, bay 41.5%, grey 8%, pseudo-Albino 3.5%, black 3.5%, brown 1% and bay roan 4%. The chest stocking color was observed as black 48%, white 33%, black and white 19%. The average body length, chest girth, neck length, ear length, and body weight in the research region were 119.86±0.17 cm, 126.28±0.19 cm, 37.87±0.14 cm, 15.78±0.12 cm, and 160.43±0.65 kg. The body length and body weight (p=0.000) differ significantly from region to region. Age at first service, service per conception (no.), gestation length, and foaling interval were 2.34±0.07 years, 1.21±0.06 years, 10.91±0.33 months and 1.00±0.03 years, respectively. Conclusions: Horses have enormous potential for livelihoods in rural areas, so it is important to preserve and improve horse genetic resources and ensure sustainable horse rearing in Bangladesh.

ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection

Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques. This paper proposes a novel deepfake detection method to improve generalizability. We observe domain-wise independent clues in deepfake images, including inconsistencies in facial colors, detectable artifacts at synthesis boundaries, and disparities in quality between facial and nonfacial regions. This approach uses an interpatch dissimilarity estimator and a multistream convolutional neural network to capture deepfake clues unique to each feature. By exploiting these clues, we enhance the effectiveness and generalizability of deepfake detection. The experimental results demonstrate the improved performance and robustness of this method.

The effect of facial colour on implicit facial expressions

ABSTRACT Humans recognise reddish-coloured faces as angry. However, does facial colour also affect “implicit” facial expression perception of which humans are not explicitly aware? In this study, we investigated the effects of facial colour on implicit facial expression perception. The experimental stimuli were “hybrid faces”, in which the low-frequency component of the neutral facial expression image was replaced with the low-frequency component of the facial expression image of happiness or anger. In Experiment 1, we confirmed that the hybrid face stimuli were perceived as neutral and, therefore, supported implicit facial expression perception. In Experiment 2, the hybrid face stimuli were adjusted to natural and reddish facial colours, and their friendliness ratings were compared. The results showed that the expression of happiness was rated as more friendly than the expression of anger. In addition, the expression of happiness was rated as friendlier when the low-frequency happy component was red, but the friendliness rating of the expression of anger did not change when it was presented in red. In Experiment 3, we affirmed the implicit facial expression perception even in reddish colours. These results suggest that facial colour modulates the perception of implicit facial expressions in hybrid facial stimuli.

A New Face Region Recovery Algorithm based on Bicubic Interpolation

Recently, researchers focused on face image manipulation detection and localization techniques because of their importance in image security applications. The previous research has not highlighted the recovery of the face region after manipulation detection. This paper presents a new face region recovery algorithm (FRRA) to be included in the face image manipulation detection algorithms (FIMD). The proposed FRRA consists of two main algorithms: face data generation algorithm and face region restoration algorithm. Both algorithms start by detecting the face region using Multi-task Cascaded Neural Network followed by a face window selection process. In the face data generation algorithm, the recovery information is generated from the shirked face window using bicubic interpolation technique. In the face region restoration algorithm, the face region zoomed using bicubic interpolation technique. The proposed FRRA has been tested and compared with previous recovery methods for different color face images, and the results proved that the FRRA could recover the face region with better visual quality at the same data length compared to previous methods. The main contributions of this research are a) the suggestion of including a face region recovery algorithm to FIMD, b) the study of previous recovery data generation algorithms for color face images, and c) introducing a new algorithm for generating the recovery data based on bicubic interpolation. In the future, the proposed algorithm can be included in the recent FIMD algorithms to recover the face region, which can be very useful in practical applications, especially those used in data forensics systems.

Accelerating Racial Activism in STEM Higher Education by Institutionalizing Equity Ethics

Background/Context: The United States invests in STEM education, but this investment is racialized and political. The country wants to maintain global economic domination, but there are also calls to diversify the STEM workforce (Baber, 2015; Basile & Lopez, 2015; Vakil & Ayers, 2019). This context leaves Black and Brown people to navigate racially hostile, toxic, and oppressive STEM learning and working environments (Bullock, 2017; Erete et al., 2020; Wright & Riley, 2021). Purpose/Objective/Research Question/Focus of Study: This conceptual paper offers strategies for institutionalizing equity ethics in STEM higher education. The paper attends to the core components of equity ethics and its possibilities while identifying structural constraints. Equity ethics is an equity-minded perspective that focuses on leveraging the multiple forms of knowledge associated with understanding and engaging the natural world to foster an oppression-free society that specifically enhances the lives of Black and Brown people. We also examine specific cases of STEM education and research scholars and practitioners who are committed to engaging and uplifting racially minoritized communities through the intersection of STEM and justice. Research Design: This conceptual paper takes a qualitative approach. It uses the existing literature to review the current state of diversity in STEM, the major components of an equity ethic, the structural and institutional barriers people of color face in STEM higher education, and how equity ethics may be institutionalized. In particular, we highlight examples of equity ethics in action to provide models for action and policy change. Conclusions/Recommendations: Institutionalized equity ethics at the mid-institutional level (e.g., departments and units) would require all faculty to mentor Black and Brown STEM students through critical transitions. We also recommend revising department- and unit-level policies and practices around access to resources that are critical for STEM achievement and knowledge production. Although expanding and sustaining financial investments in equity-focused endeavors is required, these investments must also come with the power and agency to transform institutional structures around STEM access, learning, and innovation. Transformation of top-level policies and practices could include changes to institutional admissions processes and ensuring that racist gatekeeping mechanisms are removed, while also requiring senior-level administrators to have training in racial justice ideologies and praxes. Building equity ethics into STEM higher education is essential to creating a more just and equitable STEM ecosystem.

Effectiveness Of The Omni Rate Of Perceived Exertion Color Face Scale At Self-regulating Exercise Intensity

Effectiveness Of The Omni Rate Of Perceived Exertion Color Face Scale At Self-regulating Exercise Intensity