Position Of Facial Features Research Articles

The Effects of Disrupting Holistic Processing on the Ability to Draw a Face

Previous research has demonstrated that those more skilled in drawing tend to exhibit stronger local perceptual processing biases than those less skilled in drawing. However, due to the correlational nature of this research, it is unclear whether drawing performance is facilitated by biasing perception towards local visual information. In order to investigate this, we conducted an experiment where participants drew an aligned face or a horizontally-misaligned face. Previous perceptual research has demonstrated that aligned faces are processed holistically, whereas misaligned faces are processed locally. Thus, drawings of aligned faces are assumed to be guided by holistic processing, whereas drawings of misaligned faces are assumed to be guided by local processing. Drawings were objectively measured according to the relative spatial positioning of facial features. Relative to drawings of aligned faces, the accuracy of misaligned face drawings was either impaired (for drawings of the distance between the eyes and mouth) or was not affected (for drawings of the interocular distance, the distance between the nose and mouth, and the distance between the eyes and eyebrows). This pattern of drawing errors mirrored the effects of face inversion that has previously been reported, another manipulation that is thought to disrupt holistic processing. At least with respect to drawing the relative spatial positioning of facial features, we did not observe any evidence that supports the notion that biasing perceptual processing towards local visual information directly facilitates drawing performance.

Ethnicity classification by the 3D Discrete Landmarks Model measure in Kendall shape space

Knowledge of human ethnicity constitutes important biometric information. An automated ethnicity classification is a good first step in facial analysis. However, most ethnicity classification methods require a complex feature extraction and model training process. We propose a novel ethnicity classification method based on the analysis of facial landmarks in Kendall shape space. Facial features with different relative positions have a close relationship with ethnicity. Facial landmarks can represent positions of facial features. We build a Discrete Landmarks Model (DLM) based on facial landmarks and construct an ethnicity classification model based on the DLM analysis. The clear advantages of our method are that it is fully automated; requires no complex data preprocessing, feature extraction or a complex training process; results in a fast and accurate classification process. We estimate the effectiveness of our method experimentally, using public databases such as Texas3D, FRGC2.0, BU-3DFE and BU-4DFE. On average, our method can achieve a 95% ethnicity classification rate with each classification attempt in 2.0 s.

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

3D Gaze tracking by combining eye- and facial-gaze vectors

We propose a 3D gaze-tracking method that combines accurate 3D eye- and facial-gaze vectors estimated from a Kinect v2 high-definition face model. Using accurate 3D facial and ocular feature positions, gaze positions can be calculated more accurately than with previous methods. Considering the image resolution of the face and eye regions, two gaze vectors are combined as a weighted sum, allocating more weight to facial-gaze vectors. Hence, the facial orientation mainly determines the gaze position, and eye-gaze vectors then perform minor manipulations. The 3D facial-gaze vector is first defined, and the 3D rotational center of the eyeball is then estimated; together, these define the 3D eye-gaze vector. Finally, the intersection point between the 3D gaze vector and the physical display plane is calculated as the gaze position. Experimental results show that the average gaze estimation root-mean-square error was approximately 23 pixels from the desired position at a resolution of $$1920\times 1080$$1920×1080.

Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures.

We present a nonparametric facial feature localization method using relative directional information between regularly sampled image segments and facial feature points. Instead of using any iterative parameter optimization technique or search algorithm, our method finds the location of facial feature points by using a weighted concentration of the directional vectors originating from the image segments pointing to the expected facial feature positions. Each directional vector is calculated by linear combination of eigendirectional vectors which are obtained by a principal component analysis of training facial segments in feature space of histogram of oriented gradient (HOG). Our method finds facial feature points very fast and accurately, since it utilizes statistical reasoning from all the training data without need to extract local patterns at the estimated positions of facial features, any iterative parameter optimization algorithm, and any search algorithm. In addition, we can reduce the storage size for the trained model by controlling the energy preserving level of HOG pattern space.

New method for face gaze detection in smart television

Recently, gaze detection-based interfaces have been regarded as the most natural user interface for use with smart televisions (TVs). Past research conducted on gaze detection primarily used near-infrared (NIR) cameras with NIR illuminators. However, these devices are difficult to use with smart TVs; therefore, there is an increasing need for gaze-detection technology that utilizes conventional (visible light) web cameras. Consequently, we propose a new gaze-detection method using a conventional (visible light) web camera. The proposed approach is innovative in the following three ways. First, using user-dependent facial information obtained in an initial calibration stage, an accurate head pose is calculated. Second, using theoretical and generalized models of changes in facial feature positions, horizontal and vertical head poses are calculated. Third, accurate gaze positions on a smart TV can be obtained based on the user-dependent calibration information and the calculated head poses by using a low-cost conventional web camera without an additional device for measuring the distance from the camera to the user. Experimental results indicate that the gaze-detection accuracy of our method on a 60-in. smart TV is 90.5%.

Face Recognition Techniques - A Review

Human faces are major identity mark. Face recognition is the active research area in real time applications. Face detection has many applications in biometrics, video surveillance , robotics, control of man- machines, photography, and image indexing. Many face recognition techniques are developed to recognize human features. In this paper the study and methodology of PCA (Principal Component Analysis), CoC (Coefficient of Correlation) , SSIM (Structural SIMilarity Index Metrics), DWT (Discrete Wavelet Transform) face recognition techniques is provided. I. Introduction TFace recognition is the process of identifications of the person by their facial images. this technique make it possible to use the facial images of the person to authenticate him into a secure system, for criminal identifications for passport verification etc. we can recognize a familiar individual under varying angles or viewpoints, scaling differences, different backgrounds do not affect our ability to recognize faces and we can even recognize individuals with just a fraction of their faces visible or even after several years have. Furthermore, we are able to recognize the faces of several thousand individuals whom we have met during our lifetime. The face is our primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. Although the ability to infer intelligence or character from facial appearance is suspect, the human ability to recognize faces is remarkable. We can recognize thousands of human faces learned throughout our lifetime and identify familiar faces at a glance even after years of separation. (7) This skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses or change in hairstyle or facial hair. A complete pattern recognition system consists of: i) A Sensor that gathers the observations to be classified or described, ii) A feature extraction mechanism that computes numeric or symbolic information from the observations, iii) A Classification or description scheme that does the actual job of classifying or describing observations, based on the extracted features. A face recognition system usually has a sequential configuration of processing steps: face detection, pre-processing, feature detection, feature extraction and classification. Early research of facial expression recognition needs the help of markers for facial feature point detection. The first important factor in facial recognition systems is its ability to differentiate between the background and the face. . The goal is to find a person within a large database of faces (e.g. in a police database). These systems typically return a list of the most likely people in the database. Often only one image is available per person. It is usually not necessary for recognition to be done in real-time. Changes in viewing conditions or rotations of faces bring difficulties to a face recognition system. There are two types of face rotations that must be considered here: first one is the rotation of human face images in the image-plane and the second one is the rotation of faces out of the image plane (in-depth rotation). In the first case, face images can be easily normalized by detecting several landmarks in the face and then applying some basic transformations, while in the second case such normalization is impossible since some parts of faces may be occluded. (8) Face Recognition is a term that includes several sub-problems. There are different classifications of these problems in this work. In order to build a system capable of automatically capturing facial feature positions in a face scene, the first step is to detect and extract the human face from the background image. The input of a face recognition system is always an image. The output is an identification or verification of the subject or subjects that appear in the image or video. Some approaches define a face recognition system as a three step process. From this point of view, the Face Detection and Feature Extraction phases could run simultaneously. (17)

안정적인 실시간 얼굴 특징점 추적과 감정인식 응용

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: joy, anger, and disgust.

Implementation of Mobile-Cloudlet-Cloud Architecture for Face Recognition in Cloud Computing using Android Mobile

Real time face recognition applications use face recognition algorithms, which extract information such as shape, size, and position of facial features (eyes, nose, ear, lips) and then compare this extracted feature to stored featured database to locate matching images. Face recognition application used in airport security, in passport office, in R.T.O office, in government organization, in development and research department. In this paper we implement a application which recognize a face and minimize computation time using our mobile-cloudlet-cloud architecture named MOCHA. And also develop an algorithm for percent variance. Our experimental result demonstrate that we can communicate anywhere from the world by using jelastic cloud. We will run application on mobile devices using jelastic cloud as the backend server. Keywords-Face detection, Face recognition, Jelastic cloud, Cloudlet, mobile devices

Entropy-based feature selection for improved 3D facial expression recognition

Facial expressions contain most of the information on human face which is essential for human–computer interaction. Development of robust algorithms for automatic recognition of facial expressions with high recognition rates has been a challenge for the last 10 years. In this paper, we propose a novel feature selection procedure which recognizes basic facial expressions with high recognition rates by utilizing three-Dimensional (3D) geometrical facial feature positions. The paper presents a system of classifying expressions in one of the six basic emotional categories which are anger, disgust, fear, happiness, sadness, and surprise. The paper contributes on feature selections for each expression independently and achieves high recognition rates with the proposed geometric facial features selected for each expression. The novel feature selection procedure is entropy based, and it is employed independently for each of the six basic expressions. The system’s performance is evaluated using the 3D facial expression database, BU-3DFE. Experimental results show that the proposed method outperforms the latest methods reported in the literature.

Predicting face shape from the skull using a combined NMR and stereophotographic image database of living individuals

Facial reconstruction is an artistic process with a limited scientific basis. It is a last resort in human identification inwhich an approximate facial appearance is produced fromthe skull in the hope that a resultant candidate identity might be confirmed by other means such as DNA or dental records. The aim of this investigation was to establish whether contemporary medical and stereophotographic imaging methods could be combined to predict face shape from the skull and to assess whether they can be used to reassess current guidelines for the positioning of facial features and other norms in forensic facial reconstruction. A database was collected from 60 individuals (30 male and 30 female) of predominantly White ancestry, consisting of an MRI scan of the volunteer's head and face, and corresponding 3-D stereophotographic and laser-scanned facial surface images. The skull and face surfaceswere landmarked in 3-D at up to 30 traditional anthropometric points. Accuracy in facial landmark positioning in laser, stereophotograph, and MR imageswas compared. A comparative analysis of hard and soft tissue landmarks was used to develop a method of predicting soft tissue landmark position and tissue depth from the skull of an unknown individual. Finally, the combined datasetswere used to produce a revised set of guidelines for positioning and proportions of soft-tissue features.

Sexual dimorphism in facial shapes and their discrimination in Japanese monkeys (Macaca fuscata).

The authors examined the ability of Japanese monkeys (Macaca fuscata) to discriminate between sexes based on facial features. The shape and position of facial features (facial morphology) were measured to quantify the differences between sexes. The distance between the chin and nose was longer in males than females, and the outline of the face around the upper jaw and upper face differed between sexes. Using operant conditioning, 2 monkeys succeeded in discriminating sex based on facial pictures. Furthermore, they successfully generalized the discrimination to novel pictures of faces. Tests with morphed pictures of faces revealed that the monkeys used facial morphology to discriminate between males and females. Our results suggest that Japanese monkeys have sexual dimorphism in facial shape and they can use the morphological differences to discriminate conspecific sex.

New feature based human face detection method

In the present paper, a new face model for detecting human faces in still images was developed by observing the relative geometric positions of facial features and the variations in the pixel values. These observations were converted into a set of rules which were mathematically expressed. To evaluate the performance and identify the facial characteristics in the proposed method, an algorithm was implemented and evaluated on a test set of 72 images containing 137 faces. The evaluation was based on the number of faces correctly detected and the correctness of the feature positions. The detection process was controlled by parameters that could be adjusted by users, at will, based on the characteristics of input images. The proposed method is simple, flexible, accurate and efficient. Owing to its simplicity, it can be easily implemented in most existing surveillance and automation systems.

2D 얼굴 영상을 이용한 로봇의 감정인식 및 표현시스템

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

Simultaneous reconstruction of upper and lower lip beside chin and nasal lobule: In a case of facial chemical burn

Hypertrophic scarring and contractures that distort the position of facial features and actual loss of facial parts due to burn present a unique reconstructive problem because of the wide variation of skin color and texture present [1–3]. Early reconstruction is indicated in cases with severe disfigurement of face or functional impairment. Small to medium facial skin defects can be reconstructed with local or regional flaps taken above the clavicle [2,4,5]. Large surface defects, however, often require skin grafts, distant flaps, or free flap reconstruction [6–9]. A wide variety of distant flaps now exist for facial reconstruction, although not all provide an adequate tissue match with respect to texture, color, thickness, and pliability [6–8]. Although there are various local and regional flap choices advocated in the literature for reconstruction of nasal defects [3,10–12] but usually the burn is extensive and eliminates most of potential donor sites. A very specific reconstructive problem is the facial hair-bearing skin in the male patients [13–15]. In this situation skin graft and flaps without hair bearing capacity are not suitable. Because of these subtle complexities, careful thought must be given to the selection and planning of flaps to achieve both an aesthetic and functional repair [2,6–8]. In this paper we represent our planning for reconstruction of nostrils, upper and lower lip, chin and cheek in a man with

Face detection and facial feature localization without considering the appearance of image context

Face and facial feature detection plays an important role in various applications such as human computer interaction, video surveillance, face tracking, and face recognition. Efficient face and facial feature detection algorithms are required for applying to those tasks. This paper presents the algorithms for all types of face images in the presence of several image conditions. There are two main stages. In the first stage, the faces are detected from an original image by using Canny edge detection and our proposed average face templates. Second, a proposed neural visual model (NVM) is used to recognize all possibilities of facial feature positions. Input parameters are obtained from the positions of facial features and the face characteristics that are low sensitive to intensity change. Finally, to improve the results, image dilation is applied for removing some irrelevant regions. Additionally, the algorithms can be extended to rotational invariance problem by using Radon transformation to extract the main angle of the face. With more than 1000 images, the algorithms are successfully tested with various types of faces affected by intensity, occlusion, structural components, facial expression, illumination, noise, and orientation.

Bayesian Network Enhanced Prediction for Multiple Facial Feature Tracking

It is challenging to track multiple facial features simultaneously in video while rich facial expressions are presented in a human face. To accurately predict the positions of multiple facial features' contours is important and difficult. This paper proposes a multi-cue prediction model based tracking algorithm. In the prediction model, CAMSHIFT is used to track the face in video in advance, and facial features' spatial constraint is utilized to roughly obtain the positions of facial features. Second order autoregressive process (ARP) based dynamic model is combined with graphical model (Bayesian network) based dynamic model.Incorporating ARP's quickness into graphical model's accurateness, we obtain the fusion of the prediction. Finally the prediction model and the measurement model are integrated into the framework of Kalman filter. The experimental results show that our algorithm can accurately track multiple facial features with varied facial expressions.

Gaze position detection by computing the three dimensional facial positions and motions

Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implemented it by a computer vision system with a single camera above the monitor. We computed the initial three dimensional positions of facial features (both eyes, nostrils and lip comers) by a camera calibration and a parameter estimation algorithm, and also computed the moved 3D positions of those features by 3D rotation and translation estimations and affine transform. Finally, the gaze position on the monitor is computed from the normal vector of the plane determined by those moved 3D positions of features. Gaze detection error was 5.11 cm (RMS error) when the distance between the user and the monitor is 50– 70 cm and a 19 in monitor is used.

Tracking of the motion of important facial features in model-based coding

A new method of tracking the position of important facial features for semantic wire-frame-based moving-image coding is presented. Reliable and fast tracking of the facial features in head-and-shoulders scenes is of paramount importance for reconstruction of the speaker's motion in videophone systems if wire-frame-based coding is used. The proposed method is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Motion of each facial feature (the left eye, the right eye, the nose and the lips) is tracked separately. No restrictions other than the presence of the speaker's face, were imposed on the actual contents of the scene. The algorithm was tested on widely used head-and-shoulders video sequences containing moderate head pan, rotation and zoom with remarkably good results. Tracking was maintained even when the facial features were occluded. The algorithm can also be used in other semantic-based systems.

Reliable tracking of facial features in semantic-based video coding

A new method of tracking the position of important facial features for semantic-based moving image coding is presented. Reliable and fast tracking of the facial features in head-and-shoulders scenes is of paramount importance for reconstruction of the speaker's motion in videophone systems. The proposed method is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Motion of each facial feature (the left eye, the right eye, the nose and the lips) is tracked separately; this means that the algorithm can be easily adapted for a parallel machine. No restrictions, other than the presence of the speaker's face, were imposed on the actual contents of the scene. The algorithm was tested on numerous widely used head-and-shoulders video sequences containing moderate head pan, rotation and zoom, with remarkably good results. Tracking was maintained even when the facial features were occluded. The algorithm can also be used in other semantic-based systems.