Important Facial Features Research Articles

Upper Blepharoplasty Scar and Patient Satisfaction Evaluation in a Plastic Surgery Center in Mexico

Background: Eyes are one of the most important facial features, their appearance being associated with state of mind, age and beauty. Therefore, upper blepharoplasty is one of the most common procedures performed in plastic surgery. Standardized tools allow for measurement of outcomes through patient satisfaction and surgeon experience. Objectives: Our objective was to measure the satisfaction index and scar quality of patients who underwent upper blepharoplasty using the Patient-reported Outcome Measure Questionnaire (PROM-Q) and Patient and Observer Scar Assessment Scale (POSAS). Methods: A retrospective review of patients that underwent upper blepharoplasty in our institution was performed. We included those with a preoperative anthropometric analysis, standardized incision markings (developed in our center), same operative technique and a 3-month postoperative evaluation with PROM-Q and POSAS. The sample for this study consists of 67 patients, all of them operated between January 1 2019 and June 30 2019 at the Department of Plastic Surgery, South Central High Specialty Hospital, Pemex, Mexico City, Mexico. Results: A total of 67 patients were included, 73% female and 27% male. The most frequent indication for surgery was functional visual symptoms. The index of PROM-Q at 3 months demonstrated an increased satisfaction above 90%, and the evaluation with POSAS reported an almost imperceptible scar. Conclusion: Evaluating outcomes with objective tools provides data useful to improve the surgical protocols of patients subjecting to aesthetic procedures.

Masked Face Recognition Using Deep Metric Learning and FaceMaskNet-21

The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. As the mask hides most of the face, even humans find it difficult to identify a known face. Removing the mask can risk the health of people as it can lead to the transmission of the virus. The conventional face recognition systems used for security purposes have become incapable in the current situation because, the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. The efficacy of a face recognition system is affected when trying to identify a masked face. We have proposed a system that uses the deep metric learning technique using our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, video files. The HOG technique is used to enable faster recognition of faces with a mask. We achieved an accuracy of 88.92\% with an execution time of less than 10 milliseconds. The ability of the system to perform in real-time makes it suitable to recognize people in CCTV footage in malls, banks, ATMs, etc. Our system, due to its fast performance the system can be used in schools and colleges for attendance as well as in banks and other high-security zones to grant access to only authorized ones.

Fast and Cost Efficient Face Detection System with CNN Using Raspberry PI

Face detection is an important process when it comes to computer vision. It will serve as an input to a Facial expression and Face recognition system. Modern “C.C.T.V” cameras with face detection features are costly and only few are connected to the internet. This paper proposes a Face detection system which detects faces with a fusion of Convolutional neural network and Gabor Filter. Gabor filter is used to extract important facial features and Convolutional neural network is used to train the model. Model weights files are executed in Raspberry PI which is cost efficient. Raspberry pi is connected to cloud service which will alert the user with SMS and E-mail.

Comparison between Steiner cephalometric and modified Andrews photometric method for assessing antero-posterior position of the maxillary central incisors.

Introduction/Objective. Maxillary incisors, when exposed during smile, are one of the most important facial features. In an attempt to overcome limitations of standard cephalometric methods, Andrews described an approach to determine ideal anteroposterior (AP) position of maxillary central incisors in smiling profile in relation to the forehead. We compared traditional Steiner cephalometric method, using surrounding skeletal landmarks, to the method proposed by Andrews, with the aim of determining whether distant but very noticeable craniofacial structures can affect our impression of tooth position. Methods. The study comprised 90 randomly selected lateral cephalograms, divided into three groups according to maxillary central incisors AP position according to Steiner cephalometric norms. The AP relationship of the maxillary central incisors was measured as a perpendicular distance from facial axis point to the nasion A line and to the vertical line through forehead facial axis point respectively. Student?s t-test and Pearson?s correlation were used to compare tested variables. Results. There was statistically significant difference between two methods (p = 0.01108). According to the Steiner method 46.67% subjects had retrusive incisors and 53.33% subjects had protrusion. Andrews?s method showed different results; 35.56% subjects had retrusion, while 64.4% had protrusion. Conclusion. The method proposed by Andrews showed consistently more protrusion than the traditional cephalometric method according to Steiner. Slightly retruded position of maxillary central incisors according to Steiner analysis does not always imply poor facial esthetics, if they have favorable position to the forehead. Low levels of correlation indicate that we should never rely on just one set of parameters.

A Novel Face Recognition Algorithm Using Gabor - based KPCA

The Gabor wavelets are used to extract facial features, and then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this work, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical properties of the input features, but also adopts an Eigen mask to emphasize those important facial feature points The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database.

Eyebrow Position and Shape Favored by Korean Women.

The shape and position of the eyebrows are among the most important facial features from an esthetic point of view. However, significant cultural differences exist with respect to the eyebrow shape and position that are considered ideal. The purpose of this study was to identify the position and shape of eyebrows preferred by Korean women and to apply these, including forehead lifts, to the clinics. The authors took full facial photographs in the Frankfort plane of Korean women aging from young childhood to their 50s. On the basis of these, 2 parameters were calculated: first, the height-to-width ratio (HWR), the ratio between the vertical distance from the brow apex to the palpebral fissure and the horizontal distance from the medial canthus to the lateral canthus; and second, the takeoff angle (TOA), the angle between the straight line connecting the 2 lateral canthi and the line from the brow apex through the center of the medial brow segment. The authors then had drawings of "Anastasia arch" eyebrows made; as high eyebrows are considered beautiful in Korea, and the eyebrows of women in their 20s were the highest, the drawings depicted the mean HWR and TOA values for this age group and their various degrees of deviation. These drawings were shown to 300 women aged 21 to 40 years from May 1 to June 30, 2014; they were instructed to choose the drawing they preferred among the 5 drawings differing in HWR and again among the 5 differing in TOA. Among the 99 Korean women in their 20s, the mean HWR and TOA was 1:1.01 ± 0.23 and 12° ± 5.2°, respectively. Regarding the survey, the most preferred HWR was 1:1.0, which was selected by 204 respondents (68%); the most preferred TOA was 10°, which was chosen by 122 respondents (40.7%). This study shows that HWR gradually decreases and TOA increases with age. Furthermore, Korean women's preferred eyebrows have an HWR of ∼1:1 and a TOA of ∼10°. These results could guide plastic surgeons performing periorbital rejuvenation surgeries, including brow lifts, not only for Koreans but also for North-East Asians.

Indigenous Flaps in ENT: Our Experience

The nose is undoubtedly an important facial feature. The rhinologic surgeon with interest in external nasal soft tissue surgery, oncologic or otherwise, may often require reconstruction of a soft tissue defect, from the medial canthus to the nasal ala laterally, to the nasal tip medially. The coverage needs tissues which resemble the adjoining skin texture and have a thickness which would fill the defect appropriately without appearing as a separate patch and upset the patient psychologically. Therefore we utilized local rotation and advancement flaps for the reconstruction of facial defects. Experience with these flaps is being shared. DOI: 10.21276/AABS.2017.1341

Analysis of human lip features: a review

Lip features are the most important facial features, which are being extensively used in various computer vision applications. Most of these applications like computer lip reading, lip biometrics, emotion recognition and language identification are real time applications and need robust and accurate design of lip-based recognition/classification systems. Design of such systems requires careful attention to two important issues: lip feature extraction techniques and classification methods. The motive of this review is to summarise and compare the various techniques, which have been adopted in state-of-the-art research papers to solve these issues along with related concerns. In addition, we discuss popular databases used for various lip-based applications along with performance evaluation parameters for comparison of various algorithms. This review will provide an impetus for various lip feature-based applications and new research topics, which will increase productivity in this exciting and challenging field.

Automatic acquisition of high-fidelity facial performances using monocular videos

This paper presents a facial performance capture system that automatically captures high-fidelity facial performances using uncontrolled monocular videos ( e.g ., Internet videos). We start the process by detecting and tracking important facial features such as the nose tip and mouth corners across the entire sequence and then use the detected facial features along with multilinear facial models to reconstruct 3D head poses and large-scale facial deformation of the subject at each frame. We utilize per-pixel shading cues to add fine-scale surface details such as emerging or disappearing wrinkles and folds into large-scale facial deformation. At a final step, we iterate our reconstruction procedure on large-scale facial geometry and fine-scale facial details to further improve the accuracy of facial reconstruction. We have tested our system on monocular videos downloaded from the Internet, demonstrating its accuracy and robustness under a variety of uncontrolled lighting conditions and overcoming significant shape differences across individuals. We show our system advances the state of the art in facial performance capture by comparing against alternative methods.

A Multiple Classifier System with Learning based Boosting for Clear and Occluded Color Face Identification

An automatic color face identification technique with improved identification accuracy has been designed where skin color feature are employed as an additional feature. In this system the most important facial features are extracted and face recognition is performed by individual extracted features. These decisions are combined then using learning based Boosting ensembles method. Each extracted features are used to train the system separately to learn the weights iteratively which finally boost the overall system accuracy. Experiments performed over a varied test dataset produce promising results in terms of accuracy, precision, recall and F-score for both clear as well as occluded color face images. The average accuracy of the overall system is 98.8% for clear faces and 79.31% for occluded faces. At the same time the performance evaluation time of our system is moderately low. Keywords— Face Identification; Image Processing; OCA; BP Network; RBFN; Boosting; Accuracy; Precision; Recall; F-score

Facial analysis technology aids diagnoses of genetic disorders: applications narrow down potential genetic syndromes by matching facial phenotypes to distinct set of possible genetic conditions.

Facial analysis technology aids diagnoses of genetic disorders: applications narrow down potential genetic syndromes by matching facial phenotypes to distinct set of possible genetic conditions.

Cute and Monstrous Furbys in Online Fan Production

Cute and Monstrous Furbys in Online Fan Production

Face detection and facial expression recognition using simultaneous clustering and feature selection via an expectation propagation statistical learning framework

In this paper, we focus on developing a novel framework which can be effectively used for both face detection (i.e. discriminate faces from non-face patterns) and facial expression recognition. The proposed statistical framework is based on a Dirichlet process mixture of generalized Dirichlet (GD) distributions used to model local binary pattern (LBP) features. Our method is built on nonparametric Bayesian analysis where the determination of the number of clusters is sidestepped by assuming an infinite number of mixture components. An unsupervised feature selection scheme is also integrated with the proposed nonparametric framework to improve modeling performance and generalization capabilities. By learning the proposed model using an expectation propagation (EP) inference approach, all the involved model parameters and feature saliencies can be evaluated simultaneously in a single optimization framework. Furthermore, the proposed framework is extended by adopting a localized feature selection scheme which has shown, according to our results, superior performance, to determine the most important facial features, as compared to the global one. The effectiveness and utility of the proposed method is illustrated through extensive empirical results using both synthetic data and two challenging applications involving face detection, and facial expression recognition.

Detecting Facial Expressions for Monitoring Patterns of Emotional Behavior

Detecting faces and facial expressions has become a common task in human-computer interaction systems. A face-facial detection system must be able to detect faces under various conditions and extract their facial expressions. Many approaches for face detection have been proposed in the literature mainly dealing with the detection or recognition of faces in still conditions rather than the person’s facial expressions and the reflecting emotional behavior. In this paper, the author describes a synergistic methodology for detecting frontal high-resolution color faces and for recognizing their facial expressions accurately in realistic conditions both indoor and outdoor, and with a variety of conditions (shadows, high-lights, non-white lights). The methodology associates these facial expressions to emotional behavior. It extracts important facial features, such as eyes, eyebrows, nose, mouth (lips) and defines them as the primitive elements of an alphabet of a simple formal language in order to synthesize these facial features and generate emotional expressions. The main goal of this effort is to monitor emotional behavior and learn from it. Illustrative examples are also provided for proving the concept of the methodology.

P2-22: Aging Effects on the Visual Scanning of Emotional Faces

This study investigated the effect of aging on the accuracy of response, reaction time, and visual scanning strategy, while emotional faces were viewed. Forty-three neurologically healthy participants were assigned to either a young, middle, or older-aged group. Overall, older adults were significantly less accurate in recognising facial expressions, especially those demonstrating negative emotions. Further, the young and middle-aged adults took significantly less time to recognise an emotional face than the older adults. When assessing eye movements, it was noted that the older group generated a significantly greater number of fixations to the faces and spent more time overall in looking at the various facial features. Regardless of the emotional expression and participant age, all participants looked more frequently and for longer at the eye region; this was then followed by the nose and then mouth. The findings from this work support the existence of an age-related decline in emotion recognition. However, this study is the first to document reaction time differences in identifying facial affect across three different age groups. The eyes are the most important facial feature when identifying an emotional face, and this holds true across the age ranges investigated in this work. In essence, when looking at emotional faces, middle and older-aged adults demonstrate a similar scanning pattern compared to their younger counterparts–they just take longer to do so. Therefore, the normal age-related decline in recognising facial affect appears not due to impairment in the way the eyes move to look at faces.

Effects of facial emotion recognition remediation on visual scanning of novel face stimuli

Previous research shows that emotion recognition in schizophrenia can be improved with targeted remediation that draws attention to important facial features (eyes, nose, mouth). Moreover, the effects of training have been shown to last for up to one month after training. The aim of this study was to investigate whether improved emotion recognition of novel faces is associated with concomitant changes in visual scanning of these same novel facial expressions. Thirty-nine participants with schizophrenia received emotion recognition training using Ekman's Micro-Expression Training Tool (METT), with emotion recognition and visual scanpath (VSP) recordings to face stimuli collected simultaneously. Baseline ratings of interpersonal and cognitive functioning were also collected from all participants. Post-METT training, participants showed changes in foveal attention to the features of facial expressions of emotion not used in METT training, which were generally consistent with the information about important features from the METT. In particular, there were changes in how participants looked at the features of facial expressions of emotion surprise, disgust, fear, happiness, and neutral, demonstrating that improved emotion recognition is paralleled by changes in the way participants with schizophrenia viewed novel facial expressions of emotion. However, there were overall decreases in foveal attention to sad and neutral faces that indicate more intensive instruction might be needed for these faces during training. Most importantly, the evidence shows that participant gender may affect training outcomes.

Human Face Recognition using Gabor Based Kernel Entropy Component Analysis

In this paper, the authors present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance. Firstly, from the Gabor wavelet transformed images the most important discriminative desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations due to illumination and facial expression changes were derived. After that KECA, relating to the Renyi entropy is extended to include cosine kernel function. The KECA with the cosine kernels is then applied on the extracted most important discriminating feature vectors of facial images to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having positive entropy contribution. Finally, these real KECA features are used for image classification using the L1, L2 distance measures; the Mahalanobis distance measure and the cosine similarity measure. The feasibility of the Gabor based KECA method with the cosine kernel has been successfully tested on both frontal and pose-angled face recognition, using datasets from the ORL, FRAV2D, and the FERET database.

Reassessment of Dolichopithecus (Kanagawapithecus) leptopostorbitalis, a colobine monkey from the Late Pliocene of Japan

In the original description of Dolichopithecus (Kanagawapithecus) leptopostorbitalis, Iwamoto, Hasegawa and Koizumi, 2005, a moderately large-sized colobine monkey from the Late Pliocene of central Japan, affinities to the European Dolichopithecus rather than to the Transbaikalian Parapresbytis were noted based on the similarities in cranial morphology. Computed tomography scans confirm the presence of the maxillary sinus in the holotype, whereas it is probably absent in specimens of the European Dolichopithecus ruscinensis, the type species of this genus. This feature is either present or absent homogeneously in any given genus of living anthropoids. Its presence or absence is unknown in Parapresbytis, but the distinct morphology of the maxillary incisors in this taxon suggests that this form had different feeding habits from the Japanese colobines. These findings suggest that the Japanese colobine should be referred to henceforth as Kanagawapithecus leptopostorbitalis. Kanagawapithecus shares many important facial and dental features with Dolichopithecus rather than with Parapresbytis, but this association depends largely on the limited availability of comparable materials for the latter. Among colobines, the presence of the maxillary sinus is recorded only in Libypithecus and Cercopithecoides. The maxillary sinus is absent in all modern Asian colobines, implying that Kanagawapithecus is an isolated form without any relationship to living forms. Nevertheless, such phylogenetic interpretations are largely dependent on the restricted fossil evidence from the Pliocene and Pleistocene of eastern Eurasia and will be reexamined when new findings are made.

Facial Feature Extraction Using a 4D Stereo Camera System

Facial feature recognition has received much attention among the researchers in computer vision. This paper presents a new approach for facial feature extraction. The work can be broadly classified into two stages, face acquisition and feature extraction. Face acquisition is done by a 4D stereo camera system from Dimensional Imaging and the data is available in ‘obj’ files generated by the camera system. The second stage illustrates extraction of important facial features. The algorithm developed for this purpose is inspired from the natural biological shape and structure of human face. The accuracy of identifying the facial points has been shown using simulation results. The algorithm is able to identify the tip of the nose, the point where nose meets the forehead, and near corners of both the eyes from the faces acquired by the camera system.

The Importance of Facial Features and Their Spatial Organization for Attractiveness is Modulated by Gender

Many studies suggest that facial attractiveness signals mate quality. Fewer studies argue that the preference criteria emerge as a by-product of cortical processes. One way or the other, preference criteria should not be necessarily identical between female and male observers because either their preferences may have different evolutionary roles or they may even be due to known differences in visiospatial skills and brain function lateralization (ie, advantages favoring males' inability to determine spatial relations despite distracting information). The goal of this study was to assess sex differences in face attractiveness judgments by estimating the importance of facial features and their spatial organization. To this end, semipartial correlations were measured between intact-face preferences and preferences based on specific facial parts (eyes, nose, mouth, and hairstyle) or preferences based more on configuration (as reflected by low spatial frequency images). The results show strategy modulations by both observers' and faces' genders. In general, the association between intact-face preferences and parts-based preferences was significantly higher for female compared with male participants. For female faces, males' preferences were more strongly associated with their low spatial frequency preferences than were those of females. The two genders' strategies were more similar when judging male faces, and males performed more criteria modifications across face gender. The similarities between sexes regarding male faces are in line with previous studies that showed higher assignment of importance among men to attractiveness. Moreover, the results may suggest that men adjust their strategy to assess the danger of other males as potential rivals for mates.