Facial Skin Color Research Articles

Deteksi objek manusia pada image dengan metode Thinning nerdasarkan local maxima

Tujuan dari deteksi objek manusia adalah untuk mengidentifikasi objek manusia dalam image. Adanya perubahan objek manusia yang dipengaruhi oleh faktor-faktor internal seperti ekspresi wajah, bentuk tubuh, warna kulit, dan pergerakan badan serta dipengaruhi oleh faktor eksternal seperti kondisi pencahayaan, latar belakang yang bervariasi dan sudut pengambilan image. Mendeteksi objek manusia terhadap beberapa kondisi seperti didapatkan pada image yang berisi lebih dari satu objek, dan ukuran objek yang bervariasi didalam image. Penelitian ini bertujuan untuk menganalisis dan merancang aplikaasi untuk mendeteksi objek manusia yang ada dalam image. Algoritma thinning digunakan untuk pengenalan pola objek manusia dari satu image untuk gambar yang lain agar menyamakan objek piksel untuk pendeteksian yang maksimal. Local maxima digunakan sebagai fungsi untuk memaksimumkan pencarian piksel pada objek dalam image untuk mempermudah dan mempercepat deteksi image.

Detecting Pulse Wave From Unstable Facial Videos Recorded From Consumer-Level Cameras: A Disturbance-Adaptive Orthogonal Matching Pursuit.

Modern consumer-level cameras can detect subtle changes in human facial skin color due to varying blood flow; they are beginning to be used as noncontact devices to detect pulse waves. Little, however, do we know about their capacity to perform pulse wave detection when the recorded faces are unstable. Here, we propose a novel method that can extract pulse waves from videos with drastic facial unsteadiness such as head twists and alternating expressions. The method first uses chrominance characteristics in multiple facial sub-regions to construct a raw pulse matrix. Subsequently, it employs a disturbance-adaptive orthogonal matching pursuit (DAOMP) algorithm to recover the underlying pulse matrix corrupted by facial unsteadiness. To evaluate the efficacy of the method, we perform analyses on two datasets including 268 samples from 67 testing subjects. The results demonstrate that the proposed method outperforms state-of-the-art algorithms, especially in the terrain where drastic facial unsteadiness is present. The proposed framework shows promise to achieve videos-based noncontact pulse wave detection from both steady and unsteady faces recorded by consumer-level cameras. By employing the proposed method, disturbance robustness in noncontact pulse wave detection can be significantly improved.

Development of Monitoring System for Facial Shape and Skin Color Using Depth Camera mounted on a Smartphone

Regular observation and recording of the changes in body appearance are essential for the process of the treatment of plastic surgery and dermatology, especially aesthetic surgery. Usually, physicians treat patients with medical interviews, pictures of the patient's faces before and after their treatment, anatomical data that including size, location, and color of the affected skin. However, it is difficult to capture the affected area under the same conditions every time because the captured range varies depending on the imaging angle and distance. There is a need to record three-dimensional shape of face parts such as cheek, nose, eye, and chin. Therefore, in this study, the face shape and the skin color were measured using the infrared depth camera and the RGB camera built in the smartphone three-dimensionally. We measured before and after modulating the shape and color of the face, and then, the change in volume and the change in skin pigment of skin color was calculated and visualized. This method makes it possible to analyze the skin shape and color independently of the viewing angle and the illumination direction. In this study, the depth sensor built in the smartphone showed the potential to monitor changes in facial shape and skin color. In the future, it is expected to contribute to the development of telemedicine, in which the patient measures their face at home and gets medical treatment consultation remotely.

Skin Color Perception in Portrait Image and AR-based Humanoid Emoji

Animated emoji in augmented reality (AR) enables users to create a humanoid version of themselves that mimics their facial expressions dynamically. In this study, we aim to explore how people perceive facial skin color in digital portrait in comparison with humanoid emoji in AR. We tried to identify the skin color representative regions and to estimate the color difference between the two contexts. We conducted a user study comprised of three tasks with 20 graduate students majoring in design and employed 24 portrait images in four skin tone categories. Through the user study, we first figured out that forehead and cheek regions, and particularly the linking band between eye and lip, were often considered as the representative region of facial skin color. Second, we observed skin colors become lighter in general, except dark tone. Furthermore, concerning the vidvidness, all four skin tone types became paler in humanoid emoji. Diverse ethnicities and contexts are expected in the future to provide a more robust and reliable analysis of the perception of skin color.

Exploring the Facial Color Representative Regions Using the Humanae Images

It is difficult to describe facial skin color through a solidcolor as it varies from region to region. In this article, the authors utilized image analysis to identify the facial color representative region. A total of 1052 female images from Humanae project were selected as a solid color was generated for each image as their representative skin colors by the photographer. Using the open CV-based libraries, such as EOS of Surrey Face Models and DeepFace, 3448 facial landmarks together with gender and race information were detected. For an illustrative and intuitive analysis, they then re-defined 27 visually important sub-regions to cluster the landmarks. The 27 sub-region colors for each image were finally derived and recorded in L*, a*, and b*. By estimating the color difference among representative color and 27 sub-regions, we discovered that sub-regions of below lips (low Labial) and central cheeks (upper Buccal) were the most representative regions across four major ethnicity groups. In future study, the methodology is expected to be applied for more image sources. c 2020 Society for Imaging Science and Technology.

The effect of air pollution on the skin colour and tone of Chinese women: A multicentre cohort study.

Overall facial skin colour is an important sign of perceived health and attractiveness, is predetermined by genetic factors, and is influenced by cultural and living habits, ultraviolet (UV) exposure, climate/seasons and ageing. The objective of this study was to determine the impact of pollution on the skin colour of Chinese women. A total of 203 Chinese women between 20 and 59years of age participated in the study and were selected from two cities with different levels of air pollution. Skin colour (L*, a* and b* values), melanin and haemoglobin levels were measured at three sites: the cheek, eye and inner upper arm. Measurements of the inner upper arm were taken as this area of skin was exposed to air pollutants but had minimal exposure to UV light. There were significant differences in skin chromophores between Chinese women living in two different cities with different levels of pollution. The b* value (yellowness) was higher in the eye and cheek region, and the a* value (redness) was lower in the cheek and arm region for women in the moderately polluted city. The melanin index was significantly higher, and the haemoglobin level was lower for the eye region for women living in the city with a higher level of air pollution. This study has shown that air pollution may negatively affect the skin colour of Chinese women.

High-efficiency face detection and tracking method for numerous pedestrians through face candidate generation

This paper is dedicated to developing high-efficiency face detection and tracking method for big dynamic crowds or numerous pedestrians. Three modules constitute the proposed method, i.e., face candidate generation, face candidate verification, and face target tracking. In this work, face candidates are localized using the features of the face area, edge information, and skin color. Non-face parts in the face candidates are further verified by the C-SVM learning model and then removed, by which the face targets can be generated with lower computation-complexity and satisfactory accuracy than other approaches. Finally, the face targets are tracked by an efficient and reliable searching scheme for improving the effective face detection rate. Experimental results show that the average face detection rate (FDR) of 85%, average effective FDR of 95%, a frame rate of 28–66 frames per second (fps), and about 30 faces detected per frame are obtained from various test videos with big dynamic crowds or numerous pedestrians, indicating the feasibility of the proposed method to achieve unconstrained face detection with high-efficiency and cost-effectiveness. This result makes the proposed method more attractive for the video surveillance system as compared to other approaches, especially in the high computational complexity-based methods.

Detecting Pulse Rates From Facial Videos Recorded in Unstable Lighting Conditions: An Adaptive Spatiotemporal Homomorphic Filtering Algorithm

Recent studies have shown that changes in human facial skin color due to varying blood flows can be recorded on videos by consumer-level cameras and, interestingly, can be used to measure pulse rates in a noncontact manner. However, it remains unclear whether the video-based pulse rate detection is feasible when videos are recorded in unstable lighting conditions. To address unstable illumination, a novel algorithm named adaptive spatiotemporal homomorphic filtering (ASTHF) is proposed in this article, with a focus on the separation of pulse signals and illumination-induced noise. This method first employs the homomorphic filtering framework to make illumination-induced noise in a video linearly separable. Subsequently, it incorporates a spatiotemporal filter into the framework, aiming to remove illumination-induced noise from both the time domain and the space domain of the video. To adapt to diverse illumination in the real world, the filter parameters are determined adaptively according to the sparsity in frequency spectrum of pulse signals. ASTHF is tested on two data sets, including 406 video samples. The test results show that ASTHF has stronger robustness to unstable illumination than state-of-the-art methods, improving the Pearson’s correlation coefficient by 5% in a challenging scenario compared with the best contrasting method. The proposed method demonstrates promise to achieve noncontact pulse rate detection from facial videos involving unsteady illumination.

Extracted features based multi-class classification of orthodontic images

The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.

Ethnicity Classification Based On Facial Features Using Viola-Jones Algorithm

Biometric recognition system can use race classification to identify the human globally with a particular identity. This paper proposes Support Vector Machine and will compare the result with K-Nearest Neighbor for classification of people into two major races namely Indonesian western and eastern races. Firstly, the proposed classification method extracts the distinct primary facial feature and skin color model of the given face with Viola-Jones Algorithm to effectively classify the races. To increase the accuracy, the sample must not contain any background of other people skin, no movement and the pictures were taken from the mobile camera with no beauty filter.

The Role of Sexual Selection in the Evolution of Facial Displays in Male Non-human Primates and Men

Sexual selection theory provides a framework through which some facial displays of male non-human primates can be investigated and understood. Here, we explore how both intra- and inter-sexual selection may influence facial morphology, physiology, and the behavior of male primates inhabiting diverse social and ecological environments. First, we will review how elements of the ecological environment, such as the spatio-temporal distribution of food, interacting with the diet of different species, shapes the social and mating systems of primates. In turn, these dynamics then influence how facial expressions, colors, and shapes are utilized in both the competition for and attraction of mates. We will focus on sexually dimorphic facial features that exhibit variation in their expression among males and that can be linked to differences in proxies of reproductive success. Facial displays can generally be divided into four types, each commonly associated with certain mating systems, social systems, and sexually selective pressures. Facial expressions, skin color, pelage, and variation in facial shape, can be involved in either mediating intra-sexual competition, mate choice, or a combination of both. Certain aspects of male non-human primate facial displays are likely to be shaped by processes of both intra- and inter-sexual selection. Accordingly, homologies and analogies between human and non-human primate facial displays can inform us of the processes of sexual selection that may have been operating throughout human evolution.

HYBRID APPROACH FOR HUMAN FACIAL EXPRESSION DETECTION THROUGH TLBO AND PFEF

This paper introduces facial expression detection method which is based on facial’s selected feature and optimized those selected features. The study says that human face generally faced generally consist of skin color, texture shape and size of face in this paper we study skin color and texture of human face .This process consist two steps for the same. In first known as detection of expression which uses PFEF (partial feature extension function) and in second, for optimization we used TLBO algorithm is basically a population base searching technics. Also uses soft computation technics because we cannot actual and accurate for human related activity. Varieties of technic are used for the same purpose this as per use hybrid approach to get better result.

Skin-color perception of morphed face images

The purpose of this study was to verify the interconnection between the perception of face shape and skin color. The hypothesis of this study was that, if there is a relationship between face shape and skin-color perception, a change would be expected in skin-color perception based on the degree of similarity to a face. We conducted three experiments, designed to determine the ability to retrieve from memory the color of human-like faces with several degrees of morphing. The experiments were (i) test of memory for colors of scrambled faces, (ii) test of morphing limits, and (iii) test of further degrees of morphing and more chromatic variations, based on the information gathered from the first two experiments. The first experiment indicated that a lower degree of morphing would impact the final result. The second experiment was important to determine the appropriate degree of morphing, whereas the third experiment widened the range of chromatic staring points, thereby broadening the limits of the experiment. The color-matching results differed according to the completeness of the facial shape. We found that the presence of eyes on morphed images of faces induces the perception of faces. Moreover, it appeared that the starting chromatic point, if too distant from the original stimulus, increased the difficulty of the memory task. This study has shown the possibility of a relationship between skin color and face perception.

Facial Emotion Recognition Using Hybrid Features

Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.

Objective measurement and comparison of human facial skin color in East Asian females.

Skin color is an important physiological index of human skin that has essential significance in cosmetology and dermatological diagnoses and aesthetics, especially in East Asian culture, where the beauty standard is the whiter the skin, the more beautiful a person is. However, there has been a lack of objective comparisons of facial skin color in East Asian females. Skin color at the cheek site was measured in 445 healthy females, including 362 Chinese women in Beijing and 83 Japanese women in Tokyo, all aged 18-50years. The parameters consisted of L* , a* , b* , individual typology angle (ITA°), and hue angle and were determined using noninvasive skin measurement instruments. The skin color categories in East Asians ranged from very light (I) to brown (V), and the dark (VI) category was not observed; the main categories were light (II), intermediate (III), and tan (IV). The facial skin color of Chinese individuals was brighter, more reddish, and less yellowish than that of Japanese individuals. With age, L* values decreased, and a* values and hue angle increased in East Asians with significant differences. Chinese women showed the same differences as Japanese women for all parameters in all age groups. This study provides objective skin color measurements and examines significant differences with respect to geographic location and age.

Facial Emotion Recognition using Neighborhood Features

We present a new method for human facial emotions recognition. For this purpose, initially, we detect faces in the images by using the famous cascade classifiers. Subsequently, we then extract a localized regional descriptor (LRD) which represents the features of a face based on regional appearance encoding. The LRD formulates and models various spatial regional patterns based on the relationships between local areas themselves instead of considering only raw and unprocessed intensity features of an image. To classify facial emotions into various classes of facial emotions, we train a multiclass support vector machine (M-SVM) classifier which recognizes these emotions during the testing stage. Our proposed method takes into account robust features and is independent of gender and facial skin color for emotion recognition. Moreover, our method is illumination and orientation invariant. We assessed our method on two benchmark datasets and compared it with four reference methods. Our proposed method outperformed them considering both the datasets.

An image-based mapping of significance and relevance of facial skin colour changes of females living in Thailand.

ObjectiveThere are methods to evaluate skin colour on defined areas over the face but no approach automatically and accurately evaluates skin colour variations on large facial areas, comparing subjects, treatments and/or time points. We propose such an image‐based approach to visualize quickly the outcome of clinical studies on colour variations.MethodsAmong 54 Asian women, one group applied a vehicle twice daily, during 28 days, and the other group an anti‐ageing emulsion, taking facial images at baseline and after treatment. Changes in L*a*b* values were studied on four pre‐selected facial regions. We also reconstructed average facial images from which the L*a*b* parameters were extracted for every pixel, computing relevance (ΔE) and significance data. Using colour gradients, we mapped these results onto the average facial images.ResultsAfter treatment, L*a*b* parameters show no statistically relevant colour changes in the vehicle group. In the ‘active’ group, skin was lighter at the upper cheek and, overall, redness decreased. Relevance and significance maps confirmed no visible colour changes in the vehicle group. In the ‘active’ group, the mapping approach revealed colour changes and their location. Skin became lighter below the eye, cheek and forehead. It was less red below the eyes, on the cheek, jawline and forehead, and generally more yellow.ConclusionOur image‐based mapping approach proves to be powerful. It enables us to identify precise facial regions of relevant and statistically significant colour changes after a topical treatment, regions that would have otherwise been undetected.

The Noninvasive Blood Pressure Measurement Based on Facial Images Processing

This paper proposes a noninvasive method of physiological parameter measurement with the collected facial video images for continuous online monitoring. Blood pressure (BP) is one of the important physiological indexes for human health prognoses. Based on the photoplethysmograph (PPG) technology, the variation of the facial skin color along with the heart beating can thus be captured via the camera. So the related BP information can be extracted from the blind source signal separation. The detailed procedure of the BP measurement from the facial video is explained, including video collection, face tracking, signal pre-processing, and BP signal extraction. The systolic and diastolic BP model can be derived based on the radial resonance theory and the involved parameters are fitted via the sampling data. Experiments are performed in different scenarios to discuss the impact factors on the BP detection together with error compensation analysis. The experimental results demonstrate that the proposed BP online measurement can be used as an indicator to monitor the status of the human health continuously.

Multi-face detection based on improved YCbCr color space

Face recognition has become a popular means of recognition. It is required in image retrieval, video monitoring, entrance and exit control of military bases or other sensitive areas and other occasions. Face recognition has three key steps: face detection, feature extraction and feature matching. Face detection plays an important role in face recognition. Skin color segmentation is an important part of face detection. The traditional skin color modeling rules of YCbCr color space still have some defects, so we made some improvements. We changed the parameters of the conversion matrix used in the conversion from RGB space to YCbCr color space, so that the chosen skin color region is closer to the face skin color region. By this way, the system can detect the face most accurately.

Platyrrhine color signals: New horizons to pursue.

Like catarrhines, some platyrrhines show exposed and reddish skin, raising the possibility that reddish signals have evolved convergently. This variation in skin exposure and color combined with sex‐linked polymorphic color vision in platyrrhines presents a unique, and yet underexplored, opportunity to investigate the relative importance of chromatic versus achromatic signals, the influence of color perception on signal evolution, and to understand primate communication broadly. By coding the facial skin exposure and color of 96 platyrrhines, 28 catarrhines, 7 strepsirrhines, 1 tarsiiform, and 13 nonprimates, and by simulating the ancestral character states for these traits, we provide the first analysis of the distribution and evolution of facial skin exposure and color in platyrrhini. We highlight ways in which studying the presence and use of color signals by platyrrhines and other primates will enhance our understanding of the evolution of color signals, and the forces shaping color vision.