scholarly journals CLINIFACE: PHENOTYPIC VISUALISATION AND ANALYSIS USING NON-RIGID REGISTRATION OF 3D FACIAL IMAGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 301-308
Author(s):  
R. L. Palmer ◽  
P. Helmholz ◽  
G. Baynam
Keyword(s):  
Facial Features ◽  
Facial Appearance ◽  
Facial Image ◽  
Facial Analysis ◽  
3D Images ◽  
Facial Landmarks ◽  
The Face ◽  
Measuring Tool ◽  
Facial Images ◽  
Insight Into

Abstract. Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared to expected norms. Modern photogrammetric systems can acquire detailed 3D images of the face which can be used to conduct a facial analysis in software with greater precision than can be obtained in person. Measurements from 3D facial images are already used as an alternative to direct measurement using instruments such as tape measures, rulers, or callipers. However, the ability to take accurate measurements – whether virtual or not – presupposes the assessor’s facility to accurately place the endpoints of the measuring tool at the positions of standardised anatomical facial landmarks. In this paper, we formally introduce Cliniface – a free and open source application that uses a recently published highly precise method of detecting facial landmarks from 3D facial images by non-rigidly transforming an anthropometric mask (AM) to the target face. Inter-landmark measurements are then used to automatically identify facial traits that may be of clinical significance. Herein, we show how non-experts with minimal guidance can use Cliniface to extract facial anthropometrics from a 3D facial image at a level of accuracy comparable to an expert. We further show that Cliniface itself is able to extract the same measurements at a similar level of accuracy – completely automatically.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

scholarly journals Dual Semi-Supervised Learning for Facial Action Unit Recognition

2019 ◽  
Vol 33 ◽  
pp. 8827-8834 ◽  
Author(s):  
Guozhu Peng ◽  
Shangfei Wang
Keyword(s):  
Auxiliary Information ◽  
Image Synthesis ◽  
Training Data ◽  
Facial Image ◽  
Generative Adversarial Network ◽  
Informative Feedback ◽  
Action Unit ◽  
Facial Action ◽  
The Face ◽  
Facial Images

Current works on facial action unit (AU) recognition typically require fully AU-labeled training samples. To reduce the reliance on time-consuming manual AU annotations, we propose a novel semi-supervised AU recognition method leveraging two kinds of readily available auxiliary information. The method leverages the dependencies between AUs and expressions as well as the dependencies among AUs, which are caused by facial anatomy and therefore embedded in all facial images, independent on their AU annotation status. The other auxiliary information is facial image synthesis given AUs, the dual task of AU recognition from facial images, and therefore has intrinsic probabilistic connections with AU recognition, regardless of AU annotations. Specifically, we propose a dual semi-supervised generative adversarial network for AU recognition from partially AU-labeled and fully expressionlabeled facial images. The proposed network consists of an AU classifier C, an image generator G, and a discriminator D. In addition to minimize the supervised losses of the AU classifier and the face generator for labeled training data, we explore the probabilistic duality between the tasks using adversary learning to force the convergence of the face-AU-expression tuples generated from the AU classifier and the face generator, and the ground-truth distribution in labeled data for all training data. This joint distribution also includes the inherent AU dependencies. Furthermore, we reconstruct the facial image using the output of the AU classifier as the input of the face generator, and create AU labels by feeding the output of the face generator to the AU classifier. We minimize reconstruction losses for all training data, thus exploiting the informative feedback provided by the dual tasks. Within-database and cross-database experiments on three benchmark databases demonstrate the superiority of our method in both AU recognition and face synthesis compared to state-of-the-art works.

scholarly journals The Reliability of a Three-Dimensional Photo System- (3dMDface-) Based Evaluation of the Face in Cleft Lip Infants

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Rebecca Ort ◽  
Philipp Metzler ◽  
Astrid L. Kruse ◽  
Felix Matthews ◽  
Wolfgang Zemann ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Cleft Lip ◽  
Three Dimensional ◽  
Accurate Data ◽  
3D Images ◽  
Facial Landmarks ◽  
Mean Error ◽  
Clinical Investigations ◽  
The Face ◽  
The Mean

Ample data exists about the high precision of three-dimensional (3D) scanning devices and their data acquisition of the facial surface. However, a question remains regarding which facial landmarks are reliable if identified in 3D images taken under clinical circumstances. Sources of error to be addressed could be technical, user dependent, or patient respectively anatomy related. Based on clinical 3D photos taken with the 3dMDface system, the intra observer repeatability of 27 facial landmarks in six cleft lip (CL) infants and one non-CL infant was evaluated based on a total of over 1,100 measurements. Data acquisition was sometimes challenging but successful in all patients. The mean error was 0.86 mm, with a range of 0.39 mm (Exocanthion) to 2.21 mm (soft gonion). Typically, landmarks provided a small mean error but still showed quite a high variance in measurements, for example, exocanthion from 0.04 mm to 0.93 mm. Vice versa, relatively imprecise landmarks still provide accurate data regarding specific spatial planes. One must be aware of the fact that the degree of precision is dependent on landmarks and spatial planes in question. In clinical investigations, the degree of reliability for landmarks evaluated should be taken into account. Additional reliability can be achieved via multiple measuring.

Facial feminisation: an overview of the role of the surgeon and orthodontist

2019 ◽  
Vol 46 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Kate Parker ◽  
Farhad B Naini ◽  
Daljit S Gill ◽  
Keith Altman
Keyword(s):  
Facial Features ◽  
Facial Appearance ◽  
Patient Assessment ◽  
Detailed Understanding ◽  
Good Patient ◽  
Assessment And Treatment ◽  
The Face ◽  
The Common ◽  
Male To Female

Facial feminisation surgery (FFS) aims to feminise the face by changing masculine facial features to feminine ones. It is commonly undertaken for transsexual individuals who are transitioning from male to female or for women who wish to further feminise their facial appearance. Assessment and treatment planning by a multidisciplinary team is essential for any patient considering FFS. Orthodontists have an important role within this team as patients may first present to an orthodontist expressing concerns about the appearance of their jaws. Therefore, it is important that orthodontists have a detailed understanding of FFS procedures, to enable good patient communication, thorough patient assessment and onwards referral where required. This article reviews the common FFS procedures, their indications, and the benefits and risks of each procedure and highlights the role of the orthodontist.

scholarly journals Not quite human, not quite machine: Electrophysiological responses to robot faces

2020 ◽  
Author(s):  
Allie R. Geiger ◽  
Benjamin Balas
Keyword(s):  
Neural Mechanisms ◽  
Cognitive Factors ◽  
Facial Appearance ◽  
Stimulus Class ◽  
Social Agents ◽  
Face Inversion Effect ◽  
Electrophysiological Responses ◽  
The Face ◽  
Human Faces ◽  
Insight Into

AbstractFace recognition is supported by selective neural mechanisms that are sensitive to various aspects of facial appearance. These include ERP components like the P100, N170, and P200 which exhibit different patterns of selectivity for various aspects of facial appearance. Examining the boundary between faces and non-faces using these responses is one way to develop a more robust understanding of the representation of faces in visual cortex and determine what critical properties an image must possess to be considered face-like. Here, we probe this boundary by examining how face-sensitive ERP components respond to robot faces. Robot faces are an interesting stimulus class because they can differ markedly from human faces in terms of shape, surface properties, and the configuration of facial features, but are also interpreted as social agents in a range of settings. In two experiments, we examined how the P100 and N170 responded to human faces, robot faces, and non-face objects (clocks). We found that robot faces elicit intermediate responses from face-sensitive components relative to non-face objects and both real and artificial human faces (Exp. 1), and also that the face inversion effect was only partly evident in robot faces (Exp. 2). We conclude that robot faces are an intermediate stimulus class that offers insight into the perceptual and cognitive factors that affect how social agents are identified and categorized.

scholarly journals Facelift Part I: History, Anatomy, and Clinical Assessment

2019 ◽  
Vol 40 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Ahmed M Hashem ◽  
Rafael A Couto ◽  
Eliana F R Duraes ◽  
Çagri Çakmakoğlu ◽  
Marco Swanson ◽  
...  
Keyword(s):  
Clinical Judgment ◽  
Three Dimensional ◽  
Treatment Strategies ◽  
Surgical Anatomy ◽  
Facial Appearance ◽  
Facial Aging ◽  
Facial Analysis ◽  
Safe Navigation ◽  
The Face ◽  
Neck Rejuvenation

AbstractIn this article, the authors aim to thoroughly describe the critical surgical anatomy of the facial layers, the retaining ligamentous attachments of the face, and the complex three-dimensional course of the pertinent nerves. This is supplemented with clarifying anatomic dissections and artwork figures whenever possible to enable easy, sound, and safe navigation during surgery. The historic milestones that led the evolution of cervicofacial rejuvenation to the art we know today are summarized at the beginning, and the pearls of the relevant facial analysis that permit accurate clinical judgment and hence individualized treatment strategies are highlighted at the end. The facelift operation remains the cornerstone of face and neck rejuvenation. Despite the emergence of numerous less invasive modalities, surgery continues to be the most powerful and more durable technique to modify facial appearance. All other procedures designed to ameliorate facial aging are either built around or serve as adjuncts to this formidable craft.

Judgment of Gender through Facial Parts

Perception ◽  
1995 ◽  
Vol 24 (5) ◽  
pp. 563-575 ◽  
Author(s):  
Masami K Yamaguchi ◽  
Tastu Hirukawa ◽  
So Kanazawa
Keyword(s):  
Undergraduate Students ◽  
Processing System ◽  
Race Differences ◽  
Female Undergraduate ◽  
Facial Image ◽  
Male And Female ◽  
Japanese Male ◽  
The Face ◽  
Males And Females ◽  
Facial Images

Japanese male and female undergraduate students judged the gender of a variety of facial images. These images were combinations of the following facial parts: eyebrows, eyes, nose, mouth, and the face outline (cheek and chin). These parts were extracted from averaged facial images of Japanese males and females aged 18 and 19 years by means of the Facial Image Processing System. The results suggested that, in identifying gender, subjects performed identification on the basis of the eyebrows and the face outline, and both males and females were more likely to identify the faces as those of their own gender. The results are discussed in relation to previous studies, with particular attention paid to the matter of race differences.

scholarly journals Automated syndrome diagnosis by three-dimensional facial imaging

2020 ◽  
Vol 22 (10) ◽  
pp. 1682-1693 ◽  
Author(s):  
Benedikt Hallgrímsson ◽  
J. David Aponte ◽  
David C. Katz ◽  
Jordan J. Bannister ◽  
Sheri L. Riccardi ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Genetic Syndromes ◽  
3D Images ◽  
Affected Relative ◽  
Syndrome Diagnosis ◽  
The Face ◽  
Human Faces ◽  
Facial Images ◽  
Mean Sensitivity ◽  
Deep Phenotyping

Abstract Purpose Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30–40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces. Methods We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images. Results Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative. Conclusion Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of “unaffected” relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.

scholarly journals FaceIDP: Face-IDentification Privacy under theCompressed Sensing Framework

2021 ◽  
Author(s):  
Lu Ou ◽  
Shaolin Liao ◽  
Zheng Qin ◽  
Yuan Hong ◽  
Dafang Zhang
Keyword(s):  
Differential Privacy ◽  
Real Life ◽  
Image Transmission ◽  
Face Identification ◽  
Facial Image ◽  
Scale Parameters ◽  
The Face ◽  
In The Wild ◽  
Privacy Budget ◽  
Facial Images

In FaceID era, large number of facial images could be used to breach the FaceID system, which demands effective FaceID privacy protection of the facial images for widespread adoption of FaceID technique. In this paper, to our best knowledge, we take the first step to systematically study such important FaceID privacy issue, under the framework of Compressed Sensing (CS) for fast facial image transmission. Specifically, we develop the Face-IDentification Privacy (FaceIDP) approach to protect the facial images from being used by the adversary to breach some FaceID system. First, a Dictionary Learning neural Network (DLNet) has been developed and trained with facial images database, to learn the common dictionary basis of the facial image database. Then, the encoding coefficients of the facial images are obtained. After that, the sanitizing noise is added to the encoding coefficients, which obfuscates the FaceID feature vector that is used to identify the FaceID. We have also proved that the FaceIDP is $\varepsilon$-differentially private. More importantly, optimal noise scale parameters have been obtained via the Lagrange Multiplier (LM) method to achieve better data utility for a given privacy budget $\varepsilon$. Finally, substantial experiments have been conducted to validate the efficiency of the FaceIDP with two real-life facial image databases, i.e., the LFW (Labeled Faces in the Wild) database and the PubFig database, and the results show that it outperforms other commonly used Differential Privacy (DP) approaches.

scholarly journals Evaluation of anthropometric facial landmarks in woman with Blepharophimosis, Ptosis, and Epicanthus Inversus Syndrome (BPES)

RSBO ◽  
2017 ◽  
Vol 14 (3) ◽  
pp. 147-51
Author(s):  
Lorena Maria Dering ◽  
Marina Saade ◽  
Juliana de Cassia Pinto Ferreira ◽  
Vivian Monteiro Pereira ◽  
Bruna Cristina do Nascimento Rechia ◽  
...  
Keyword(s):  
Observational Study ◽  
Anthropometric Measurements ◽  
Facial Appearance ◽  
Bilateral Ptosis ◽  
Facial Analysis ◽  
Oral Region ◽  
Facial Landmarks ◽  
Non Invasive ◽  
Facial Anthropometry ◽  
Caucasian Female

Blepharophimosis, ptosis, and epicanthus inversus syndrome (BPES) is a syndrome easily recognized by facial appearance. In this sense, the facial anthropometry is a simple and non-invasive way to evaluate the morphology of the facial surface of individuals, thus, defining the craniofacial dimensions. Objective: To evaluate the facial anthropometric measurements of a Caucasian female, aged 20 years, diagnosed with BPES and to compare these measures with the values described in the literature for non-syndromic woman. Material and methods: This research is an observational study of a Caucasian female, aged 20 years, who was diagnosed with BPES. Frontal photographs were taken, and the images analyzed by nine researchers calibrated in Image J® software. The facial measurements evaluated were head, face, orbits, nose, and labio-oral region and were compared with non-syndromic woman. Results: All vertical and horizontal face measurements were higher than that of other females from Caucasian groups. BPES woman also presented bilateral ptosis and the main differences appear in the region of the orbits. Conclusion: The anthropometric facial analysis of BPES woman showed a significant change in the facial landmarks.

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