A framework for investigating the use of face features to identify spontaneous emotions

Emotion-based analysis has raised a lot of interest, particularly in areas such as forensics, medicine, music, psychology, and human-machine interface. Following this trend, the use of facial analysis (either automatic or human-based) is the most common subject to be investigated once this type of data can easily be collected and is well accepted in the literature as a metric for inference of emotional states. Despite this popularity, due to several constraints found in real world scenarios (e.g. lightning, complex backgrounds, facial hair and so on), automatically obtaining affective information from face accurately is a very challenging accomplishment. This work presents a framework which aims to analyse emotional experiences through naturally generated facial expressions. Our main contribution is a new 4-dimensional model to describe emotional experiences in terms of appraisal, facial expressions, mood, and subjective experiences. In addition, we present an experiment using a new protocol proposed to obtain spontaneous emotional reactions. The results have suggested that the initial emotional state described by the participants of the experiment was different from that described after the exposure to the eliciting stimulus, thus showing that the used stimuli were capable of inducing the expected emotional states in most individuals. Moreover, our results pointed out that spontaneous facial reactions to emotions are very different from those in prototypic expressions due to the lack of expressiveness in the latter.

Prediction of difficult intubation with advanced face recognition techniques: a preliminary study

Introduction: Difficult tracheal intubation remains a constant and significant source of morbidity and mortality in anaesthetic practice. Insufficient airway assessment in the preoperative period continues to be a major cause of unanticipated difficult intubation. Although many risk factors have already been identified, preoperative airway evaluation is not always regarded as a standard procedure and the respective weight of each risk factor remains unclear. Moreover the predictive scores available are not sensitive, moderately specific and often operator-dependant. In order to improve the preoperative detection of patients at risk for difficult intubation, we developed a system for automated and objective evaluation of morphologic criteria of the face and neck using video recordings and advanced techniques borrowed from face recognition. Method and results: Frontal video sequences were recorded in 5 healthy volunteers. During the video recording, subjects were requested to perform maximal flexion-extension of the neck and to open wide the mouth with tongue pulled out. A robust and real-time face tracking system was then applied, allowing to automatically identify and map a grid of 55 control points on the face, which were tracked during head motion. These points located important features of the face, such as the eyebrows, the nose, the contours of the eyes and mouth, and the external contours, including the chin. Moreover, based on this face tracking, the orientation of the head could also be estimated at each frame of the video sequence. Thus, we could infer for each frame the pitch angle of the head pose (related to the vertical rotation of the head) and obtain the degree of head extension. Morphological criteria used in the most frequent cited predictive scores were also extracted, such as mouth opening, degree of visibility of the uvula or thyreo-mental distance. Discussion and conclusion: Preliminary results suggest the high feasibility of the technique. The next step will be the application of the same automated and objective evaluation to patients who will undergo tracheal intubation. The difficulties related to intubation will be then correlated to the biometric characteristics of the patients. The objective in mind is to analyze the biometrics data with artificial intelligence algorithms to build a highly sensitive and specific predictive test.

Visualizing key authenticity: turning your face into your public key

La informació biomètrica s'ha convertit en una tecnologia complementària a la criptografia que permet administrar còmodament les dades criptogràfiques. Són útils dues necessitats importants: en primer lloc, posar aquestes dades sempre a mà i, a més, fent fàcilment identificable el seu legítim propietari. En aquest article es proposa un sistema que integra la signatura biomètrica de reconeixement facial amb un esquema de signatura basat en la identitat, de manera que la cara de l'usuari esdevé la seva clau pública i la ID del sistema. D'aquesta manera, altres usuaris poden verificar els missatges utilitzant fotos del remitent, proporcionant un intercanvi raonable entre la seguretat del sistema i la usabilitat, així com una manera molt més senzilla d'autenticar claus públiques i processos de distribució.

Thoracic and heart biometrics of non-anesthetized agouti (Dasyprocta primnolopha Wagler, 1831) measured on radiographic images

The agouti is a species intensively hunted throughout the Amazon and the semi-arid regions of northeastern Brazil. Considering the current trend in conservation management of wild species, the aim of this study was to determine the morphometric reference to the heart of agouti raised in captivity, based on thoracic and cardiac measurements in these animals. Thirty adult agoutis, 1 to 3 years of age, without clinical signs of cardiac disease were selected. The animals were physically restrained and radiographies in laterolateral (LL) and ventrodorsal (VD) recumbence were produced. The following measures were taken: the apicobasilar length of the heart (at the most cranial height of the Carina region to the heart apex) (AB), maximum width of the heart perpendicular to AB (CD), heart inclination angle (AIC), trachea inclination angle (AIT), distance from the right heart wall (DPTd), distance from the left heart wall (DPTe) and vertical depth of the thorax, and the ventral face of the vertebral column to the dorsal border of the sternum at the level of the trachea bifurcation (H). The ratios between AB/CD, AB/H and CD/H were also analyzed. To calculate the vertebral heart scale (VHS), the AB and CD measurements were laid over the thoracic vertebra starting at T4. Radiographic evaluation showed values consistent with those reported in small animals and some wild and exotic species. The main biometric values in the chest cavity and heart of agouti are arranged as follows: (1) The ratios between AB/H ratio and CD/H were not sensitive for identifying heart increases (p>0.05), while the ratio AB/CD was more sensitive in this identification (p<0.05); (2) AIC: 21.2±6.4º (mean between male and famale); (3) AIT for males and females: 9.93±3.23° and 8.4±3.94°; (4) DPTd and DPTe for males: 0.97±0.40cm and 0.7±0.30cm; (5) DPTd and DPTe for females: 1.12±0.42cm and 01.02±0.43cm; (6) VHS for males and females: 7.75±0.48v e 7.61±0.34v; (7) The caudal vena cava (CVC) was visualized dorsal-cranially and located right of the midline. The data obtained allowed the acquisition of the first reference values for biometry of the heart of agoutis, contributing to better understanding of cardiac morphology and identification of cardiomyopathy in these animals.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems

Biometrics in ABC: counter-spoofing research

Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

An study on ear detection and its applications to face detection

[EN]OpenCV includes di erent object detectors based on the Viola-Jones framework. Most of them are specialized to deal with the frontal face pattern and its inner elements: eyes, nose, and mouth. In this paper, we focus on the ear pattern detection, particularly when a head pro le or almost pro le view is present in the image. We aim at creating real-time ear detectors based on the general object detection framework provided with OpenCV. After training classi ers to detect left ears, right ears, and ears in general, the performance achieved is valid to be used to feed not only a head pose estimation system but also other applications such as those based on ear biometrics.

Face and eye detection on hard datasets

[EN] This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors. Performance is characterized under the different conditions and parameterized by per-image brightness and contrast. In localization accuracy for eyes, the groups/companies focusing on long-range face detection outperform leading commercial applications.

An integrative approach to face and expression recognition from 3D scans

Biometrics is afield of study which pursues the association of a person's identity with his/her physiological or behavioral characteristics.^ As one aspect of biometrics, face recognition has attracted special attention because it is a natural and noninvasive means to identify individuals. Most of the previous studies in face recognition are based on two-dimensional (2D) intensity images. Face recognition based on 2D intensity images, however, is sensitive to environment illumination and subject orientation changes, affecting the recognition results. With the development of three-dimensional (3D) scanners, 3D face recognition is being explored as an alternative to the traditional 2D methods for face recognition.^ This dissertation proposes a method in which the expression and the identity of a face are determined in an integrated fashion from 3D scans. In this framework, there is a front end expression recognition module which sorts the incoming 3D face according to the expression detected in the 3D scans. Then, scans with neutral expressions are processed by a corresponding 3D neutral face recognition module. Alternatively, if a scan displays a non-neutral expression, e.g., a smiling expression, it will be routed to an appropriate specialized recognition module for smiling face recognition.^ The expression recognition method proposed in this dissertation is innovative in that it uses information from 3D scans to perform the classification task. A smiling face recognition module was developed, based on the statistical modeling of the variance between faces with neutral expression and faces with a smiling expression.^ The proposed expression and face recognition framework was tested with a database containing 120 3D scans from 30 subjects (Half are neutral faces and half are smiling faces). It is shown that the proposed framework achieves a recognition rate 10% higher than attempting the identification with only the neutral face recognition module.^

Thermal imaging as a biometrics approach to facial signature authentication

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the thermal mid-wave infrared portion of the electromagnetic spectrum. The goals of this research is to design specialized algorithms that would extract facial vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal infrared imaging. The proposed thermal facial signature recognition is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through testing our algorithm on a database, referred to as C-X1, provided by the Computer Vision Research Laboratory at the University of Notre Dame. Feature extraction was accomplished by first registering the infrared images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal infrared images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate thermal signatures and a thermal template for each subject, the thermal template contains only the most prevalent and consistent features. Finally a similarity measure technique was used to match signatures to templates and the Principal Component Analysis (PCA) was used to validate the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using an Euclidean-based similarity measure showed 88% accuracy in the case of skeletonized signatures and templates, we obtained 90% accuracy for anisotropically diffused signatures and templates. We also employed the Manhattan-based similarity measure and obtained an accuracy of 90.39% for skeletonized and diffused templates and signatures. It was found that an average 18.9% improvement in the similarity measure was obtained when using diffused templates. The Euclidean- and Manhattan-based similarity measure was also applied to skeletonized signatures and templates of 25 subjects in the C-X1 database. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the thermal infrared system to be used on other thermal imaging based systems and related databases. A novel user-initialization registration of thermal facial images has been successfully implemented. Furthermore, the novel approach at developing a thermal signature template using four images taken at various times ensured that unforeseen changes in the vasculature did not affect the biometric matching process as it relied on consistent thermal features.

Epidemiologia do Padrão Face Longa em escolares do Ensino Fundamental do município de Bauru - SP

OBJETIVO: o objetivo deste trabalho foi classificar e determinar a prevalência dos indivíduos portadores de comprometimento vertical nas relações faciais, conforme a severidade da discrepância e, especialmente, dos portadores de Padrão Face Longa. MÉTODOS: a amostra constou de 5.020 sujeitos de nacionalidade brasileira, de ambos os sexos, com idades entre 10 anos e 16 anos e 11 meses, matriculados no Ensino Fundamental do município de Bauru-SP. O exame da morfologia facial constou da observação direta da face, em normas frontal e lateral, sempre com os lábios em repouso, buscando identificar aqueles indivíduos que apresentassem comprometimento vertical nas relações faciais. Esses, uma vez identificados, foram classificados, considerando-se a severidade, em três subtipos: moderado, médio e grave. Para fins de determinação da prevalência dos portadores de Padrão Face Longa, apenas os classificados como dos subtipos médio e grave foram considerados. RESULTADOS: observou-se uma prevalência de 34,94% de comprometimento vertical nas relações faciais e 14,06% de Padrão Face Longa. CONCLUSÃO: os resultados obtidos no presente estudo permitiram concluir que a prevalência do comprometimento vertical nas relações faciais e do Padrão Face Longa foi maior do que a presumida com base na literatura.

Estudo das características cefalométricas em adolescentes brasileiros portadores de Padrão Face Longa

OBJETIVO: definir valores cefalométricos esqueléticos e dentários para adolescentes brasileiros com Padrão Face Longa. MÉTODOS: a amostra foi constituída de telerradiografias em norma lateral de 30 pacientes com Face Longa, sendo 17 do sexo feminino e 13 do masculino; e 30 pacientes face Padrão I, 15 do sexo masculino e 15 do feminino, no estágio de dentadura permanente durante a adolescência. As características do Padrão Face Longa foram definidas clinicamente, pela análise facial. As seguintes grandezas cefalométricas foram avaliadas: (1) Comportamento sagital das bases apicais (SNA, SNB, ANB, NAP, Co-A, Co-Gn); (2) Comportamento vertical das bases apicais (SN.PP, SN.PM, ângulo goníaco, AFAT, AFAI, AFAM, AFP, AFATperp, AFAIperp); (3) Comportamento dentoalveolar (1-PP, 6-PP, 1-PM, 6-PM, 1.PP, IMPA); e (4) Proporção entre as alturas faciais (AFAIPerp/AFATPerp, AFAI/AFAT, AFAM/AFAI). RESULTADOS E CONCLUSÕES: o erro vertical na Face Longa concentra-se no terço inferior. A maxila apresenta uma maior altura dentoalveolar e a mandíbula, com morfologia mais vertical, mostra maior rotação no sentido horário. Essas características morfológicas e espaciais acarretam alterações sagitais e verticais no esqueleto e alterações verticais dentoalveolares. No sentido sagital, os ângulos de convexidade facial estão aumentados. No sentido vertical, as alturas faciais anteriores total e inferior estão aumentadas. O componente dentoalveolar está mais longo.

A novel polar-based human face recognition computational model

Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

Influence of physical and geometrical parameters on three-dimensional load transfer mechanism at tunnel face

Three-dimensional discretizations used in numerical analyses of tunnel construction normally include excavation step lengths much shorter than tunnel cross-section dimensions. Simulations have usually worked around this problem by using excavation steps that are much larger than the actual physical steps used in a real tunnel excavation. In contrast, the analyses performed in this study were based on finely discretized meshes capable of reproducing the excavation lengths actually used in tunnels, and the results obtained for internal forces are up to 100% greater than those found in other analyses available in the literature. Whereas most reports conclude that internal forces depend on support delay length alone, this study shows that geometric path dependency (reflected by excavation round length) is very strong, even considering linear elasticity. Moreover, many other solutions found in the literature have also neglected the importance of the relative stiffness between the ground mass and support structure, probably owing to the relatively coarse meshes used in these studies. The analyses presented here show that relative stiffness may account for internal force discrepancies in the order of 60%. A dimensionless expression that takes all these parameters into account is presented as a good approximation for the load transfer mechanism at the tunnel face.