Facial Skin Color Research Articles

Facial skin characteristics and concerns in Indonesia: A cross-sectional observational study.

Facial skin characteristics and appearance vary according to ethnicity. While much of this knowledge is derived from the Caucasian population, lately there have been efforts to gain such understanding in various regions in Asia.In this paper, we have built an understanding of such features in Indonesia. In Indonesia, a section of females wears a traditional veil (hijab) to cover the scalp and part of face. The influence of the hijab on facial skin attributes was also investigated. Ina cross-sectional observational study designinvolving 419 female volunteers in Jakarta, Indonesia, facial skin attributes (colour, radiance, hydration, trans-epidermal water loss [TEWL], wrinkles, fine lines, pores, and sebum levels) and conditions (melasma, post-inflammatory hyperpigmentation (PIH), solar lentigines/ senile lentigines, seborrheic keratoses and acne) were assessed by trained operators and dermatologists using standard validated instruments and scales. With age, facial skin colour showed darkening in cheek; forehead on the other hand showed slight lightening. The skin evenness and radiance decreased, substantially. Aging attributes measured in terms of lines, wrinkles, and under-eye dark circles showed deterioration with age; the decline was progressively faster than colour change. Facial image data analysis corroborated these findings.Skin hydration remained similar across the age groups even though the skin barrier function measured in terms of TEWL improved with age. Sebum levels in the skin were similar up to the age of 50 but declined in the next group of 50-60 year.Pore severity increased with age. Melasma, seborrheic keratosis and PIH showed a high prevalence (>∼50%) at the young age group (20-30 years), itself. Melasma prevalence attained 100% in the age group of 41-50 year and onwards, and its severity similarly showed a steady rise with age. PIH on the other hand showed a steady decline with age. Solar lentigines prevalence (∼30%) did not change much across age groups, and the severity scores were similar in age groups up to 50 year but increased substantially in 51-60-year age groups. Seborrheic keratosis was similar (∼47%) in age groups up 20-40 year but steadily increased in upper age groups.Its severity was similar in the age groups of 20-30 year and 31-40 year but showed a two-fold increase in subsequent age groups. Acne was 10% in the age group of 20-30 year and declined gradually to 0.7% in the 51-60-year age group. Hijab wearers showed slight protection in skin colour darkening and improvement of evenness and radiance but were similar on aging (fine lines and wrinkles on crow's feet, under eye and peri-oral areas) markers to non-wearers. In general, in majority of age-groups, hijab wearers showed a higher prevalence of melasma, solar/senile lentigines, seborrheic keratosis and PIH.

Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning.

BackgroundAs human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming more prominent. The susceptibility of people to MS can be accurately assessed, which will allow ordinary people to choose comfortable transportation and entertainment and prevent people susceptible to MS from entering provocative environments. This is valuable for maintaining public health and the safety of tasks.ObjectiveTo develop an objective multi-dimensional MS susceptibility assessment model based on physiological indicators that objectively reflect the severity of MS and provide a reference for improving the existing MS susceptibility assessment methods.MethodsMS was induced in 51 participants using the Coriolis acceleration stimulation. Some portable equipment were used to digitize the typical clinical manifestations of MS and explore the correlations between them and Graybiel's diagnostic criteria. Based on significant objective parameters and selected machine learning (ML) algorithms, several MS susceptibility assessment models were developed, and their performances were compared.ResultsGastric electrical activity, facial skin color, skin temperature, and nystagmus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%.ConclusionThe severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and objective assessment models for MS susceptibility based on ML can be successfully established.

Effective of a novel technique for sensitive skin treatment with optimal pulse technology: A clinical study.

Photomodulation is a non-photothermal effect that mobilizes energy and regulates cell activity at the mitochondrial level, and has been used to treat sensitive skin (SS) in recent years. Based on the photomodulated effect of optimal pulse technology (OPT), we developed a novel treatment mode (advanced OPT with low energy, three pulses, long pulse width, AOPT-LTL) for the treatment of facial SS and evaluated its effectiveness. A total of thirty Chinese women with SS were included in this study. Patients were received different times of AOPT-LTL treatment with an interval of 2 to 4weeks depending on the severity. Clinical improvement was evaluated by comparing baseline and post-treatment photographs. In addition, the skin objective signs and subjective symptoms, as well as adverse events and patient satisfaction were also analyzed and tabulated. All included patients completed the treatment and follow-up period. After one course of treatment, 76.7% of patients had a Symptom Score Reducing Index (SSRI) >20%, suggesting that the treatment was effective. Within two courses of treatment, all patients had SSRI >20%, demonstrating significant improvement in skin sensitivity. The analysis of clinical photographs showed that facial dryness, desquamation, flushing, and skin color significantly improved, capillary density decreased, the dilated capillaries were retracted. During the treatment period, no obvious adverse reactions occurred in any patients, and the patients' satisfaction was high. This novel technique of AOPT-LTL might be an effective and safe modality for the treatment of SS.

Reconstruction of a massive facial defect with the pre-expanded free anterolateral thigh flap: A case report.

Introduction and importanceMassive facial defects remain a reconstructive challenge because of the region's unique character and the limitation of a well-matched donor site. Pre-expanded free anterolateral thigh (ALT) flap provides a good alternative for reconstructing massive skin and soft tissue defects.Case presentationA 21-year-old male patient presented to our department with a massive right facial degloving wound caused by a sharp edged-weapon attack. The patient left the hospital after 3 weeks of wound care, and only came back to us after 2 months with large defects at the nasal, cheek, and upper and lower right lip regions. The procedure of ALT flap expansion surgery was performed for 2 months. The pre-expanded ALT flap was used for reconstruction of the patient's facial defects. Two-year follow-up showed that the flap at the reconstructed area resembled the contour of the nasal tip, facial skin color similarities in the cheeks and lips, and the patient's mouth had normal function.Clinical discussionThe combined pre-expanded and composite ALT free flap techniques allow simultaneous reconstruction of many different anatomic units. Flap debulking helps to improve the nasal contour from the original defect. The lip separation technique and flap debulking procedure help enhance aesthetic reconstructive outcomes of the skin flap.ConclusionThe surgical reconstruction using a pre-expanded ALT free flap for the patient with a massive facial defect was safe. In particular, sufficient skin and soft tissue ensured facial aesthetics and oral function for the patient when using this method.

Skin Color Match in Head and Neck Reconstructive Surgery.

To quantify the degree of color match achieved during microvascular facial reconstruction, and to describe a novel technique for improving reconstructive skin color match. We hypothesize that split-thickness skin grafts (STSG) placed atop de-epithelialized free tissue produces better facial skin color match than free tissue with intact epithelium. Cross sectional photographic study of reconstructed facial skin color match. Sixty-eight adults, who underwent head and neck reconstructive surgery, were divided into six categories based on cutaneous reconstructive technique: cervicofacial flap, radial forearm free flap (RFFF), fibula free flap, anterolateral thigh free flap (ALT), STSG over adiopofascial flap (STAFF), and STSG over myogenous flap (STMF). Averaged color samplings of the reconstructed defect and adjacent normal skin were taken from digital photographs. The color difference was calculated using the delta-E calculation. Blinded expert observers also rated the degree of color match. Nonparametric cohort contrast and correlation statistical analyses were performed. The mean delta-E's and 10-point Likert ratings for the ALT, fibula, RFFF, STAFF, STMF, and cervicofacial flaps were 11.6, 10.0, 7.7, 6.3, 8.8, and 4.7, and 5.1, 6.4, 2.4, 3.2, 2.7, and 1.1, respectively. Likert scale inter-rater correlation was strong, with coefficient=0.80. On average, STSG over de-epithelialized myogenous and adipofascial free tissue transfers produced a better color match than the skin paddles of donor sites, with the exception of the radial forearm donor site. Delta-E values obtained from photos correlated well with expert ratings of color match. This reliable technique for quantifying color match may be used in future studies. 3 Laryngoscope, 132:1753-1759, 2022.

Investigating factors of metabolic bone disease in baboons (Papio spp.) using museum collections.

ObjectivesTo assess manifestations of metabolic bone disease (MBD) and their potential environmental and phenotypic factors in captive and non‐captive baboon (Papio spp.) specimens.Materials and methodsOur sample consisted of 160 baboon specimens at the Smithsonian's National Museum of Natural History accessioned from 1890 to 1971. Combining cranial indicators of MBD and the museum's historical data, we examined factors contributing to likely instances of MBD. We used binomial‐family generalized linear models to assess differences in MBD frequency by environment (captive, non‐captive), specimen accession year, and skin color (light, medium, dark).ResultsIndicators of MBD were most frequently observed in captive baboons, with a decrease in MBD frequency over time. Fifteen non‐captive individuals showed indicators of MBD, which are the first published cases of MBD in non‐captive nonhuman primates (NHPs) to our knowledge. The most common MBD indicators were bone porosity (n = 35) and bone thickening/enlargement (n = 35). Fibrous osteodystrophy was observed frequently in our sample, likely relating to nutritional deficiencies. We found no association between exposed facial skin color variation and MBD.ConclusionsOur findings are consistent with historical accounts of MBD prevalence in captive facilities, especially earlier in the 20th century. A decrease in MBD prevalence later in the 20th century likely reflects improvements in housing, diet, and veterinary care in captive settings. Causes of MBD development in non‐captive baboons should be further explored, as understanding the potential health impacts that anthropogenic environments impose on NHPs is imperative as humans increasingly alter the natural world in the 21st century.

Facial skin colour classification using machine learning and hyperspectral imaging data

Facial skin colour is a primary indicator of human health, and skin care products aim to improve it. However, the beauty industry lacks accurate methods to automatically classify skin colour because it uses high granularity labels such as the Pantone scale, with up to 110 facial skin-colour types. In this study, the automatic classification of facial skin colour based on hyperspectral imaging and machine learning is investigated. An experiment is conducted using hyperspectral imaging to collect multi-dimensional big data on most of these skin-colour types. Owing to the multi-dimensionality of the data and the high granularity of skin-colour types, classifying the colour type accurately is challenging. Nevertheless, various machine-learning methods are applied and it is found that each had an advantage in categorizing a subset of colour types. Further, the features of the data are analysed and it is found that skin chromaticity and brightness could be classified separately to provide valuable information. Finally, to utilise this information and combine the advantages of various machine-learning methods, a two-stage integrated classifier is developed, which achieved 90.4% accuracy. This classifier can be used by the beauty industry to evaluate the effect of skincare products on facial skin colour.

Skin coloration is a culturally-specific cue for attractiveness, healthiness, and youthfulness in observers of Chinese and western European descent.

Facial skin coloration signals information about an individual and plays an important role in social interactions and mate choice, due its putative association with health, attractiveness, and age. Whether skin coloration as an evolutionary significant cue is universal or specific to a particular culture is unclear and current evidence on the universality of skin color as a cue to health and attractiveness are equivocal. The current study used 80 calibrated, high-resolution, non-manipulated images of real human faces, either of Chinese or western European descent, which were rated in terms of attractiveness, healthiness, and perceived age by 44 observers, 22 western European (13 male; mean age ± SD = 24.27 ± 5.30) and 22 Chinese (7 male; mean age ± SD = 26.05 ± 3.96) observers. To elucidate the associations between skin coloration and these perceptual ratings and whether these associations are modulated by observer or image ethnicity, a linear mixed-effect model was setup with skin lightness (L*; CIELAB), redness (a*) and yellowness (b*), observer and image ethnicity as independent variables and perceived attractiveness, healthiness, and estimated age as dependent variables. We found robust positive associations between facial skin lightness (L*) and attractiveness, healthiness, and youthfulness, but only when Chinese observers judge facial images of their own ethnicity. Observers of European descent, on the other hand, associated an increase in yellowness(b*) with greater attractiveness and healthiness in Chinese facial images. We find no evidence that facial redness is positively associated with these attributes; instead, an increase in redness (a*) is associated with an increase in the estimated age of European facial images. We conclude that observers of both ethnicities make use of skin color and lightness to rate attractiveness, healthiness, and perceived age, but to a lesser degree than previously thought. Furthermore, these coloration cues are not universal and are utilized differently within the Chinese and western European ethnic groups. Our study adds to the growing body of work demonstrating the importance of skin color manipulations within an evolutionary meaningful parameter space, ideally using realistic skin models based on physical parameters.

GWAS Identifies Multiple Genetic Loci for Skin Color in Korean Women

Human skin color is largely determined by genetic factors. Recent GWASs have reported several genetic variants associated with skin color, mostly in European and African populations. In this study, we performed GWAS in 17,019 Korean women to identify genetic variants associated with facial skin color, quantitatively measured as CIELAB color index. We identified variants in three, one, and six genomic loci associated with facial skin color index L∗, a∗, and b∗ values, respectively, and replicated the associations (combined analysis P-value < 5.0× 10-8). The significant loci included variants in known genes (OCA2 rs74653330, BNC2 rs16935073, rs72620727 near KITLG, and SLC6A17 rs6689641) and to our knowledge previously unreported genes (SCARB1 rs10846744, SYN2 rs12629034, and LINC00486 rs6543678). This is GWAS to elucidate genetic variants of facial skin color in a Korean female population. Further functional characterizations of the investigated genes are warranted to elucidate their contribution to skin pigmentation-related traits.

Development of an image‐based measurement system for human facial skin colour

AbstractIn this study, an image‐based measurement system was developed for human facial skin colour, involving the development of a digital imaging system, collection of facial skin colour from 60 human subjects, generation of different colour characterization models, and performance evaluation. The factors that affect facial skin colour characterization, including different training datasets (two colour charts and the collected facial skin colour dataset), mathematical mapping methods (linear transformation, polynomial regression, root‐polynomial regression and neural network) and camera image formats (JPG and RAW), were investigated and quantified not only by the conventional method of CIELAB colour difference, but also two newly introduced measures, facial colour contrast and skin colour gamut. The results indicate that the RAW image format for camera digital signals gave a more stable performance than the JPG format images, and the higher order polynomial regression with good predictive accuracy in terms of CIELAB colour difference did not perform well for the whole facial image. It is suggested to evaluate the model performance using the colour of both specific facial positions and the overall facial skin colour. Our comparative analysis in this study provides useful guidance for determining the colour characterization model for facial skin.

Guided Facial Skin Color Correction

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.

In elderly Caucasian women, younger facial perceived age correlates with better forearm skin microcirculation reactivity.

Visual and molecular changes occurring upon aging are rather well characterized. Still, aging signs show great significant inter-individual variations, and little is known concerning the link between perceived age and cutaneous microcirculation. To investigate this point, we recruited Caucasian women in their mid-50's to mid-70's and subsampled women looking older or younger than their age. We studied their facial skin color, as well as their microvascular reactivity to local heating assessed in the forearm skin. We also used skin biopsies from some of these women for gene expression or immunohistochemical analysis. Clinical and instrumental analysis of skin color revealed that subjects who look 5years younger differ only by a higher glowing complexion. Our most striking result is that subjects looking 5years younger than their age present a higher microcirculation reactivity in forearm skin. Transcriptome comparison of skin samples from women looking older or younger than their age revealed 123 annotated transcripts differentially expressed, among which MYL9 relates to microcirculation. MYL9 is downregulated in the group of women looking younger than their real age. Microscopy shows that the labeling of MYL9 and CD31 are altered and heterogeneous with age, as is the morphology of microvessels. Therefore, assessing generalized vascular reactivity in non-photo-exposed skin to focus on the intrinsic aging allows subtle discrimination of perceived age within elderly healthy subjects.

Prevalence of Gingival Pigmentation and its Association with Gingival Biotype and Skin Colour

Introduction: The facial appearance depends on several oral and extraoral factors including colour of facial skin and pigmentation of gingival epithelium. The colour of the gingiva varies among individuals and is thought to be associated with cutaneous pigmentation which ranges from light to dark brown or black colour.&#x0D; Objective: To assess the prevalence of physiological gingival pigmentation, gingival biotype and their association with skin colur in Nepalese subjects visiting Kantipur Dental College and Hospital (KDCH).&#x0D; Methods: This was an analytical cross-sectional study which was carried out from February 2020 to June 2020 in all patients of age-group 16 to 80 years visiting the Department of Periodontics at KDCH after ethical approval. Patients were recruited by convenience sampling and examined thoroughly to find out gingival biotype and extent of gingival pigmentation intraorally as well as skin colour extraorally.&#x0D; Results: In this study, 210 patients were examined among which, 105 (50%) were males and 105 (50%) were females. Out of 210, 33 (15.7%) had pink tissue without pigmentation, 84 (40%) had pigmentation only in attached gingiva, 58 (27.6%) in attached gingiva and interdental papilla, 32 (15.2%) had diffuse pigmentation involving all parts of gingiva, 2 (1%) had in marginal gingiva only, and 1 (0.5%) in marginal gingiva and interdental papilla.&#x0D; Conclusion: A strong association was found between gingival pigmentation and facial skin colour in present study (P &lt;0.001). Establishing the pattern of gingival pigmentation in Nepalese population will help to choose a specific depigmentation therapy that will harmonise with skin colour.

Real Time Heart Rate Monitoring from Facial Video using Independent Component Analysis Algorithm

Heart Rate (HR) is one of the most important Physiological parameter and a vital indicator of people’s physiological state and is therefore important to monitor. Monitoring of HR often involves high costs and complex application of sensors and sensor systems. Research progressing during last decade focuses more on noncontact based systems which are simple, low-cost and comfortable to use. Still most of the noncontact based systems are fit for lab environments in offline situation but needs to progress considerably before they can be applied in real time applications. This project presents a real time HR monitoring method using a webcam of a laptop computer. The heart rate is obtained through facial skin color variation caused by blood circulation. Some of the signal processing methods such as Fast Fourier Transform (FFT), Independent Component Analysis (ICA) have been applied on the color channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. The obtained results show that there is a high degrees of agreement between the proposed experiments and reference measurements. This technology has significant potential for advancing personal health care and telemedicine. Further improvements of the proposed algorithm considering environmental illumination and movement can be very useful in many real time applications such as driver monitoring. This can be used for gaming purpose where the real heart rate of the person can be felt for the character present in game.

Recognition of Human Face Emotions Detection Using Computer Vision Based Smart Images

Humans share a collection of basic and essential emotions that are expressed by facial expressions that seem to be consistent. The automated identification of humanemotion in imageswill be possible due to an algorithm that detects, extracts, and evaluates these facial expressions.The Face detector and recognizer application is a desktop application used to recognize human face emotions by using a computer vision based smart images. It consists of human face detection picture boxes and considers an image as an original image. It climaxes a face skin color, finds high impact area and identifies different emotions of the face from an image. The results of the image are depending upon the separation of Eyes &amp; Lips movement of person. This is done by comparingface embedding vectors. It finds the smart photos focused on computer vision for successful identification of facial emotions in terms of various modes of speech such as smiling, shocking and weeping.

Automatic Classification of Adult Males With and Without Autism Spectrum Disorder by Non-contact Measurement of Autonomic Nervous System Activation.

People with autism spectrum disorder (ASD) exhibit atypicality in various domains of behavior. Previous psychophysiological studies have revealed an atypical pattern of autonomic nervous system (ANS) activation induced by psychosocial stimulation. Thus, it might be feasible to develop a novel assessment tool to evaluate the risk of ASD by measuring ANS activation in response to emotional stimulation. The present study investigated whether people with ASD could be automatically classified from neurotypical adults based solely on physiological data obtained by the recently introduced non-contact measurement of pulse wave. We video-recorded faces of adult males with and without ASD while watching emotion-inducing video clips. Features reflective of ANS activation were extracted from the temporal fluctuation of facial skin coloration and entered into a machine-learning algorithm. Though the performance was modest, the gradient boosting classifier succeeded in classifying people with and without ASD, which indicates that facial skin color fluctuation contains information useful for detecting people with ASD. Taking into consideration the fact that the current study recruited only high-functioning adults who have relatively mild symptoms and probably developed some compensatory strategies, ASD screening by non-contact measurement of pulse wave could be a promising assessment tool to evaluate ASD risk.

Comparisons in perception of facial skin brightness, as influenced by differences in skin color: Asian observers

AbstractHuman skin color is one of the most common colors that we see and perceive in everyday life. Previous research on skin color distribution in young Japanese women has revealed that yellowish skin has higher lightness than reddish skin. Furthermore, reddish skin appears brighter than yellowish skin when both have the same lightness (Yoshikawa et al., 2012). Despite these findings, it is unclear how brightness perception of facial skin color is influenced by the diversity of skin colors and observers. The present study investigated the perception of facial skin color among observers in three Asian countries. A young Japanese female face was used, and test faces of four skin color shades were prepared, corresponding to the average skin colors of Japanese, Thai, Caucasian, and African individuals. A test image (with constant lightness but different hue angles) and a scale image (with the original hue but varying lightness) were presented side‐by‐side on a color‐calibrated tablet display, and observers adjusted the brightness of the facial skin of the scale image to match that of the test image. The findings indicated that Japanese observers showed a trend in which reddish skin was perceived as brighter than yellowish skin; however, Thai and Chinese observers did not show this trend. This implies the potential influence of ethnicities or environments on brightness perception of facial skin.

A novel camera colour characterisation model for the colour measurement of human skin

Accurate facial skin colour representation is highly required for an increasing number of applications, such as the solution of cosmetic products, the diagnosis of cutaneous diseases, and the manufacture of soft tissue prostheses. This study presents a novel camera colour characterisation model with higher predictive accuracy for the image-based colour measurement of human skin. More specifically, a digital imaging system was developed to collect the facial images of sixty human subjects from four ethnic groups. The newly collected human facial skin colour data and a conventional colour chart were selected as the training dataset, respectively, and three general techniques (linear transformation, polynomial regression, and root-polynomial regression) were utilised to derive the characterisation model by mapping camera digital signals to CIE XYZ tristimulus values. The predictive accuracy of each model was then verified using the mean CIELAB colour difference between actual skin colour measurements and the corresponding predictions from colour images. Results showed that the best model performance was achieved when the human skin colours of real subjects were used as the training samples and first order polynomial regression was used as the mapping algorithm.

Facial micro-expressions as a soft biometric for person recognition

• Micro-expressions contain information pertinent to person recognition. • LBP-TOP is well suited to model appearance and motion changes in micro-expression. • Optimal weight distribution is important for traditional and soft biometric fusion. Soft biometrics, although not discriminant enough for person recognition provides additional information that aids traditional person recognition. Initially, attempts were made to integrate appearance-based facial soft biometrics, such as facial marks, skin color, and hair color/style, but more recently behavior-based facial soft biometrics, such as head dynamics, visual speech, and facial expressions have also been studied. Facial expressions are further classified as macro and micro-expressions and most of the existing studies using facial expressions as a soft biometric have focused on macro-expressions. Therefore, in this study, we investigate the utility of micro-expressions as a soft biometric for person recognition. The proposed system is based on the fusion of traditional facial features that model the facial appearance with soft biometric features that model the micro-expressions in an image sequence. We tested a texture-based traditional feature extraction technique, two motion-based soft biometric techniques, and several fusion methods at feature, rank, and decision level. The experiments were conducted on three commonly used micro-expression databases and exhibit an improvement of around 5% identification rate when soft biometric traits are fused with traditional face recognition at decision level.

PCA-ANN Contactless Multimodality Sensors for Body Temperature Estimation

The use of the infrared (IR) forehead thermometer is a convenient way to measure the body temperature; however, it exhibits the concerns of accuracy, reliability, and repeatability because of improper measurement poses such as distance and angle to forehead, and other variations of subject and ambient conditions. According to the advantages of quick response and contactless measurement of using IR thermometry, this article additionally considers the factors of distance to IR sensors, subject’s facial skin color, and forehead pose, as well as ambient temperature and light intensity, to propose an accurate regression for body temperature estimation. As a consequence, the proposed multimodality sensor (MMS) system consists of a set of thermopile IR sensors, a distance sensor, a light intensity sensor, and a web camera. Furthermore, a principal component analysis (PCA) approach combining the artificial neural network is utilized for the body temperature regression. In order to reduce the measurement bias, the digital axillary temperature data was used as the ground body temperature for the machine learning training and validation. The MMS system is set randomly close to the forehead at a distance of 2–30 cm to emulate the human’s operation bias. We have totally 30 subjects for this study, and we divided them into four groups as: training dataset (14 subjects, IDs: 1–14 with 300 cases), validation dataset (three subjects, IDs: 15–17 with 64 cases), concurrent-time test dataset (three subjects, IDs: 18–20 with 64 cases), and three-months-elapsed test dataset (ten subject, IDs: 21–30 with 240 cases). In the training process, the $k$ -fold cross-validation technique ( $k = 5$ ) was applied to reduce the model overfitting. In addition, multiple linear regression (MLR) and stepwise regression (SR) models were also developed for performance comparison. The result shows that the PCA-ANN temperature estimation model outperformed other predictive models with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) values, and highest $R$ values and can optimize the low-cost MLX90614 sensor accuracy. The three-months-elapsed test group achieves MAE $0.1~^{\circ }\text{C}$ with a standard deviation of the error $0.03~^{\circ }\text{C}$ when compared to the state-of-the-art with low cost and contactless IR sensors.